KEYWORDS: Image segmentation, Ultrasonography, Data modeling, Breast, Tunable filters, Performance modeling, Medical imaging, Image sharpness, Statistical modeling, Education and training
Ultrasound imaging is a powerful imaging modality for diagnosing breast tumors due to its non-invasive nature, real-time imaging capabilities, and lack of ionizing radiation. Ultrasound imaging has certain limitations that can make it demanding to detect masses compared to other imaging modalities. Therefore, breast ultrasound image segmentation is a crucial and challenging task in computer-aided diagnosis (CAD) systems. Deep learning (DL) has revolutionized medical image segmentation. Among DL models, UNet architecture is widely used for its exceptional performance. This study assesses the effectiveness of sharpening filters and attention mechanisms between the decoder and encoder in UNet models for breast ultrasound segmentation. Combining Sharp UNet and Attention UNet, we propose a novel approach called Parallel Sharp Attention UNet (PSA_UNet). A public dataset of 780 cases was utilized in this study. The results are promising for the proposed method, with the Dice coefficient and F1 score of 0.93 and 0.94, respectively. McNemar's results show that our proposed model outperforms the earlier designs upon which our model is based. In addition to introducing a new network, this study highlights the importance of optimization and finetuning in improving UNet-based segmentation models. The results offer potential improvements in breast cancer diagnosis and treatment planning through more accurate and efficient medical image segmentation techniques.
This study explores reverberant shear wave elastography to create accurate magnetic resonance elastograms. The reverberant elastography technique utilizes the complex wave field originating from multiple point sources or reflected from various angles and superimposed with each other. The study was conducted on a calibrated brain phantom. Results showed that reverberant elastography produced accurate elastograms with an accuracy range of 84-97% and contrast-to-noise ratios of 24 dB, compared to an accuracy range of 86-97.7% and contrast-to-noise ratios of 25 dB for the established subzone inversion method.
The term "elastography" covers a dynamic and expanding set of diagnostic imaging techniques which probe the biomechanical properties of tissues. This overview covers some of the major approaches that have evolved and their role in improving clinical diagnoses. A deeper level of study is also emerging linking our estimates of viscoelasticity to the multiscale structure and composition of living tissue in normal and diseased states. These studies can be undertaken at the highest spatial resolution with optical techniques, and examples in cornea, brain, and skin will be covered.
The reverberant shear wave (RSW) technique offers a promising framework for elastography. In this study, to characterize fibrotic fatty livers at different fibrotic stages, we employed an autocorrelation (AC) estimator within the RSW framework to evaluate shear wave speed (SWS) of viscoelastic media. To this end, we utilized both simulation and experimental approaches and excited the RSW field in a medium within each approach at the frequency of 150 Hz: (i) the finite element (FE) simulation of a RSW field in a 3D model of a whole organ fatty liver and (ii) the RSW experiments on two castoroil- in-gelatin phantoms fabricated in the lab. In the FE simulations, to represent a more realistic liver model, a thin adipose fat layer and a muscle layer were added as viscoelastic power-law materials on top of the liver model. The SWS estimation from the RSW simulation was compared with predictions from the theory of composite media for verification. For the RSW experiments on phantoms, the SWS estimations were compared with the SWS results obtained from performing the stress relaxation test as an independent modality. The simulation results showed that the RSW-based AC estimator provides good estimates of SWS, within >90% accuracy compared with theory. Also, the RSW estimator results from the phantom experiments at different background stiffness levels provided some experimental support for the utility of the RSW estimator. These results demonstrated that the AC estimator is sensitive to the changes in viscoelastic properties of viscoelastic media.
Purpose: The study of speckle from imaging systems has a rich history, and recently it was proposed that a fractal or power law distribution of scatterers in vascularized tissue will lead to a form of the Burr probability distribution functions for speckle amplitudes. This hypothesis is generalized and tested in theory, simulations, and experiments.
Approach: We argue that two broadly applicable conjectures are sufficient to justify the applicability of the Burr distribution for speckle from a number of acoustical, optical, and other pulse-echo systems. The first requirement is a multiscale power law distribution of weak scatterers, and the second is a linear approximation for the increase in echo intensity with size over some range of applicability.
Results: The Burr distribution for speckle emerges under a wide variety of conditions and system parameters, and from this one can estimate the governing power law parameter, commonly in the range of 2 to 6. However, system effects including the imaging point spread function and the degree of focusing will influence the Burr parameters.
Conclusions: A generalized pair of conditions is sufficient for producing Burr distributions across a number of imaging systems. Simulations and some theoretical considerations indicate that the estimated Burr power law parameter will increase with increasing density of scatters. For studies of speckle from living tissue or multiscale natural structures, the Burr distribution should be considered as a long tail alternative to classical distributions.
A recent theoretical framework using power-law functions was proposed to model scattering from biological tissues in ultrasound and optical coherence tomography. Multi-scale scattering sites such as the fractal branching vasculature will then contribute to power-law based probability distributions of speckle statistics. These distributions are the Burr type XII distribution, the Lomax distribution, and the generalized logistic distribution for speckle amplitude, intensity, and log amplitude, respectively. Previous experiments with ultrasound and optical coherence tomography demonstrate that these distributions are better fits to image histogram data of various biological tissues when compared with classical models (e.g., Rayleigh, K, and gamma distributions). Of critical importance is that this framework provides novel parameters, most notably the power-law exponent parameter, for characterizing the physics of scattering from soft tissue. The typical range for the exponent parameter in other normal tissues is approximately 3 to 6. The aim is for this parameter to be used as a new biomarker for diagnostic imaging, sensitive to changes in tissue structures. Here, we demonstrate a specific application to mouse brain tissue, in which the exponent parameter is used to characterize mouse cortical brain under various conditions including ex vivo and in vivo using optical coherence tomography.
Significance: Corneal cross-linking (CXL) is a well-known procedure for treating certain eye disorders such as keratoconus. However, characterization of the biomechanical changes in the cornea as a result of this procedure is still under active research. Specifically, there is a clinical need for high-resolution characterization of individual corneal layers.
Aim: A high-resolution elastography method in conjunction with a custom optical coherence tomography system is used to track these biomechanical changes in individual corneal layers. Pre- and post-treatment analysis for both low-dose and high-dose CXL experiments are performed.
Approach: A recently developed elastography technique that utilizes the theory of reverberant shear wave fields, with optical coherence tomography as the modality, is applied to pig corneas ex vivo to evaluate elasticity changes associated with corneal CXL. Sets of low-dose and high-dose CXL treatments are evaluated before and after treatments with three pairs of pig corneas per experiment.
Results: The reverberant three-dimensional (3D) optical coherence elastography (OCE) technique can identify increases in elasticity associated with both low-dose and high-dose CXL treatments. There is a notable graphical difference between low-dose and high-dose treatments. In addition, the technique is able to identify which layers of the cornea are potentially affected by the CXL procedure and provides insight into the nonlinearity of the elasticity changes.
Conclusions: The reverberant 3D OCE technique can identify depth-resolved changes in elasticity of the cornea associated with CXL procedures. This method could be translated to assess and monitor CXL efficacy in various clinical settings.
Purpose: Recent theories examine the role of the fractal branching vasculature as a primary site of Born scattering from soft normal tissues. These derivations postulate that the first-order statistics of speckle from soft tissue, such as the liver, thyroid, and prostate, will follow a Burr distribution with a power law parameter that can be related back to the underlying power law, which governs the branching network. However, the issue of scatterer spacing, or the number of cylindrical vessels per sample volume of the interrogating pulse, has not been directly addressed.
Approach: Speckle statistics are examined with a 3D simulation that varies the number density and the governing power law parameter of an ensemble of different sized cylinders. Several in vivo liver scans are also analyzed for confirmation across different conditions.
Results: The Burr distribution is found to be an appropriate model for the histogram of amplitudes from speckle regions, where the parameters track the underlying power law and scatterer density conditions. These results are also tested in a more general model of rat liver scans in normal versus abnormal conditions, and the resulting Burr parameters are also found to be appropriate and sensitive to underlying scatterer distributions.
Conclusions: These preliminary results suggest that the classical Burr distribution may be useful in the quantification of scattering of ultrasound from soft vascularized tissues and as a tool in tissue characterization.
Longitudinal shear waves (LSW) are waves with longitudinally polarized displacement that travel at the shear wave speed through depth when generated at the surface of tissues. In this study, we explore LSW generated by a circular glass plate in contact with the sample. Results demonstrated the potential of LSW in detecting an elasticity gradient along axial (resolution < 0.13 mm) and lateral (resolution < 0.78 mm) directions simultaneously. Finally, LSWs are used for the elastography of ex vivo mouse brain and demonstrated differentiated LSW speed values between the cerebral cortex (2.91 m/s) and cerebellum/midbrain (1.18 m/s) regions.
In medicine, both pathological (e.g. cirrhosis) and non-pathological states (e.g. aging) can be characterized by changes in the mechanical properties of biological tissue. The use of optical techniques to measure and map the elastic properties of soft tissue, known as optical elastography, is an emergent field with applications in various clinical disciplines, including ophthalmology and dermatology. In this paper, a brief overview of optical elastography will be provided with a short taxonomy. Categories include appropriate types of tissue models (semi-infinite, single thin layer, composite stacks), clinical tasks (classification or estimation), and excitation modes (transient, continuous, quasi-static, or molecular shift). We will then discuss examples of current advances, including optical coherence elastography using reverberant shear wave fields and Brillouin microscopy. The examples will demonstrate how current and future techniques may address clinical needs. Advantages and disadvantages of these techniques will be presented, augmenting the framework of the categorization system. With emerging applications, the taxonomy may be expanded providing a roadmap to future techniques.
A number of approaches employ optical coherence tomography (OCT) to obtain the mechanical properties of biological tissue. These are generally referred to as optical coherence elastography (OCE), and have demonstrated promising applications with studies in cornea, breast, muscle, skin, and other soft tissues. A particular application of interest is the brain, in which changes in local and global elastic properties may correlate with the onset and progression of degenerative brain diseases. In this preliminary study, mice brains are studied ex vivo and in situ with preservation of the brain/skull anatomical architecture. A small 6 mm diameter portion of the skull is replaced with a glass cap to allow for OCT imaging. Various permutations of source placement for generating shear waves and modes of excitation are evaluated to optimize the experimental setup. The use of reverberant shear wave fields, which takes advantage of inevitable reflections from boundaries and tissue inhomogeneities, allow for estimation of the shear wave speed, which is directly related to the elastic modulus of soft tissues. Preliminary estimates for the shear wave speed in brains of recently deceased mice are obtained. This study demonstrates potential applications in brain OCE ex vivo and in vivo.
Determining the mechanical properties of tissue such as elasticity and viscosity is fundamental for better understanding and assessment of pathological and physiological processes. Dynamic optical coherence elastography uses shear/surface wave propagation to estimate frequency-dependent wave speed and Young’s modulus. However, for dispersive tissues, the displacement pulse is highly damped and distorted during propagation, diminishing the effectiveness of peak tracking approaches. The majority of methods used to determine mechanical properties assume a rheological model of tissue for the calculation of viscoelastic parameters. Further, plane wave propagation is sometimes assumed which contributes to estimation errors. To overcome these limitations, we invert a general wave propagation model which incorporates (1) the initial force shape of the excitation pulse in the space-time field, (2) wave speed dispersion, (3) wave attenuation caused by the material properties of the sample, (4) wave spreading caused by the outward cylindrical propagation of the wavefronts, and (5) the rheological-independent estimation of the dispersive medium. Experiments were conducted in elastic and viscous tissue-mimicking phantoms by producing a Gaussian push using acoustic radiation force excitation, and measuring the wave propagation using a swept-source frequency domain optical coherence tomography system. Results confirm the effectiveness of the inversion method in estimating viscoelasticity in both the viscous and elastic phantoms when compared to mechanical measurements. Finally, the viscoelastic characterization of collagen hydrogels was conducted. Preliminary results indicate a relationship between collagen concentration and viscoelastic parameters which is important for tissue engineering applications.
The H-scan analysis of ultrasound images is a matched-filter approach derived from analysis of scattering from incident pulses in the form of Gaussian-weighted Hermite polynomial functions. This framework is applied in a preliminary study of thyroid lesions to examine the H-scan outputs for three categories: normal thyroid, benign lesions, and cancerous lesions within a total group size of 46 patients. In addition, phantoms comprised of spherical scatterers are analyzed to establish independent reference values for comparison. The results demonstrate a small but significant difference in some measures of the H-scan channel outputs between the different groups.
In the H-scan analysis and display, visualization of different scattering sizes and types is enabled by a matched filter approach involving different orders of Gaussian weighted Hermite functions. An important question with respect to clinical applications involves the change in H-scan outputs with respect to small changes in scatterer sizes. The sensitivity of H-scan outputs is analyzed using the theory of backscatter from a compressible sphere. Experimental corroboration is established using mono dispersed spherical scatterers in phantoms. With a 6-MHz center frequency broadband transducer, it is possible to visualize changes in scattering size in the order of 10 to 15 μm in phantoms and also changes in ex vivo bovine liver tissue due to edema caused by hypotonic perfusion.
Ultrasound B-scan imaging systems operate under some well-known resolution limits. To improve resolution, the concept of stable pulses, having bounded inverse filters, was previously utilized for the lateral deconvolution. This framework has been extended to the axial direction, enabling a two-dimensional deconvolution. The modeling of the two-way response in the axial direction is discussed, and the deconvolution is performed in the in-phase quadrature data domain. Stable inverse filters are generated and applied for the deconvolution of the image data from Field II simulation, a tissue-mimicking phantom, and in vivo imaging of a carotid artery, where resolution enhancement is observed. Specifically, in simulation results, the resolution is enhanced by as many as 8.75 times laterally and 20.5 times axially considering the −6-dB width of the autocorrelation of the envelope images.
We compare five optical coherence elastography techniques able to estimate the shear speed of waves generated by one and two sources of excitation. The first two techniques make use of one piezoelectric actuator in order to produce a continuous shear wave propagation or a tone-burst propagation (TBP) of 400 Hz over a gelatin tissue-mimicking phantom. The remaining techniques utilize a second actuator located on the opposite side of the region of interest in order to create three types of interference patterns: crawling waves, swept crawling waves, and standing waves, depending on the selection of the frequency difference between the two actuators. We evaluated accuracy, contrast to noise ratio, resolution, and acquisition time for each technique during experiments. Numerical simulations were also performed in order to support the experimental findings. Results suggest that in the presence of strong internal reflections, single source methods are more accurate and less variable when compared to the two-actuator methods. In particular, TBP reports the best performance with an accuracy error <4.1%. Finally, the TBP was tested in a fresh chicken tibialis anterior muscle with a localized thermally ablated lesion in order to evaluate its performance in biological tissue.
Many pulse-echo imaging systems use focused beams to improve lateral resolution. The beam width is determined by the choice of source and apodization function, the frequency, and the physics of focusing. Postprocessing strategies to improve lateral resolution can be limited by the need for conditioning the mathematics of inverse filtering, due to instabilities. We present an analysis that defines key constraints on sampled versions of lateral beampatterns. Within these constraints are useful symmetric beampatterns, which, when properly sampled, can have a stable inverse filter. A framework for analysis and processing is described and applied to phantoms and tissues to demonstrate the improvements that can be realized.
Optical Coherence Elastography (OCE) is a widely investigated noninvasive technique for estimating the mechanical properties of tissue. In particular, vibrational OCE methods aim to estimate the shear wave velocity generated by an external stimulus in order to calculate the elastic modulus of tissue. In this study, we compare the performance of five acquisition and processing techniques for estimating the shear wave speed in simulations and experiments using tissue-mimicking phantoms. Accuracy, contrast-to-noise ratio, and resolution are measured for all cases. The first two techniques make the use of one piezoelectric actuator for generating a continuous shear wave propagation (SWP) and a tone-burst propagation (TBP) of 400 Hz over the gelatin phantom. The other techniques make use of one additional actuator located on the opposite side of the region of interest in order to create an interference pattern. When both actuators have the same frequency, a standing wave (SW) pattern is generated. Otherwise, when there is a frequency difference df between both actuators, a crawling wave (CrW) pattern is generated and propagates with less speed than a shear wave, which makes it suitable for being detected by the 2D cross-sectional OCE imaging. If df is not small compared to the operational frequency, the CrW travels faster and a sampled version of it (SCrW) is acquired by the system. Preliminary results suggest that TBP (error < 4.1%) and SWP (error < 6%) techniques are more accurate when compared to mechanical measurement test results.
Various types of waves are produced when a harmonic force is applied to a semi-infinite half space elastic medium. In particular, surface waves are perturbations with transverse and longitudinal components of displacement that propagate in the boundary region at the surface of the elastic solid. Shear wave speed estimation is the standard for characterizing elastic properties of tissue in elastography; however, the penetration depth of Optical Coherence Tomography (OCT) is typically measured in millimeters constraining the measurement region of interest to be near the surface. Plane harmonic Rayleigh waves propagate in solid-vacuum interfaces while Scholte waves exist in solid-fluid interfaces. Theoretically, for an elastic solid with a Poisson’s ratio close to 0.5, the ratio of the Rayleigh to shear wave speed is 95%, and 84% for the Scholte to shear wave. Our study demonstrates the evidence of Rayleigh waves propagating in the solid-air boundary of tissue-mimicking elastic phantoms. Sinusoidal tone-bursts of 400Hz and 1000 Hz were excited over the phantom by using a piezoelectric actuator. The wave propagation was detected with a phase-sensitive OCT system, and its speed was measured by tracking the most prominent peak of the tone in time and space. Similarly, this same experiment was repeated with a water interface. In order to obtain the shear wave speed in the material, mechanical compression tests were conducted in samples of the same phantom. A 93.9% Rayleigh-shear and 82.4% Scholte-Shear speed ratio were measured during experiments which are in agreement with theoretical results.
A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than 5 ng/ml and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than 5 ng/ml. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.
Understanding the spatial distribution of prostate cancer and how it changes according to prostate specific antigen (PSA) values, Gleason score, and other clinical parameters may help comprehend the disease and increase the overall success rate of biopsies. This work aims to build 3D spatial distributions of prostate cancer and examine the extent and location of cancer as a function of independent clinical parameters. The border of the gland and cancerous regions from wholemount histopathological images are used to reconstruct 3D models showing the localization of tumor. This process utilizes color segmentation and interpolation based on mathematical morphological distance. 58 glands are deformed into one prostate atlas using a combination of rigid, affine, and b-spline deformable registration techniques. Spatial distribution is developed by counting the number of occurrences in a given position in 3D space from each registered prostate cancer. Finally a difference between proportions is used to compare different spatial distributions. Results show that prostate cancer has a significant difference (SD) in the right zone of the prostate between populations with PSA greater and less than 5ng/ml. Age does not have any impact in the spatial distribution of the disease. Positive and negative capsule-penetrated cases show a SD in the right posterior zone. There is SD in almost all the glands between cases with tumors larger and smaller than 10% of the whole prostate. A larger database is needed to improve the statistical validity of the test. Finally, information from whole-mount histopathological images may provide better insight into prostate cancer.
KEYWORDS: Tissues, Cancer, Prostate, Prostate cancer, Image processing, Signal to noise ratio, Image filtering, Signal attenuation, Ultrasonography, Digital filtering
Crawling wave (CrW) sonoelastography is an elasticity imaging technique capable of estimating the localized shear
wave speed in tissue and, therefore, can provide a quantitative estimation of the Young's modulus for a given vibration
frequency. In this paper, this technique is used to detect cancer in excised human prostates and to provide quantitative
estimations of the viscoelastic properties of cancerous and normal tissues. Image processing techniques are introduced to
compensate for attenuation and reflection artifacts of the CrW images. Preliminary results were obtained with fifteen
prostate glands after radical prostatectomy. The glands were vibrated at 100, 120 and 140Hz. At each frequency, three
cross-sections of the gland (apex, mid-gland and base) were imaged using CrW Sonoelastography and compared to
corresponding histological slices. Results showed good spatial correspondence with histology and an 80% accuracy in
cancer detection. In addition, shear velocities for cancerous and normal tissues were estimated as 4.75±0.97 m/s and
3.26±0.87 m/s, respectively.
The capability of sonoelastography to detect lesions based on elasticity contrast can be applied to monitor the creation of
thermally ablated lesion. Currently, segmentation of lesions depicted in sonoelastographic images is performed manually
which can be a time consuming process and prone to significant intra- and inter-observer variability. This work presents
a semi-automated segmentation algorithm for sonoelastographic data. The user starts by planting a seed in the perceived
center of the lesion. Fast marching methods use this information to create an initial estimate of the lesion. Subsequently,
level set methods refine its final shape by attaching the segmented contour to edges in the image while maintaining
smoothness. The algorithm is applied to in vivo sonoelastographic images from twenty five thermal ablated lesions
created in porcine livers. The estimated area is compared to results from manual segmentation and gross pathology
images. Results show that the algorithm outperforms manual segmentation in accuracy, inter- and intra-observer
variability. The processing time per image is significantly reduced.
This paper assesses lesion contrast and detection using sonoelastographic shear velocity imaging. Shear wave
interference patterns, termed crawling waves, for a two phase medium were simulated assuming plane wave conditions.
Shear velocity estimates were computed using a spatial autocorrelation algorithm that operates in the direction of shear
wave propagation for a given kernel size. Contrast was determined by analyzing shear velocity estimate transition
between mediums. Experimental results were obtained using heterogeneous phantoms with spherical inclusions (5 or 10
mm in diameter) characterized by elevated shear velocities. Two vibration sources were applied to opposing phantom
edges and scanned (orthogonal to shear wave propagation) with an ultrasound scanner equipped for sonoelastography.
Demodulated data was saved and transferred to an external computer for processing shear velocity images. Simulation
results demonstrate shear velocity transition between contrasting mediums is governed by both estimator kernel size and
source vibration frequency. Experimental results from phantoms further indicates that decreasing estimator kernel size
produces corresponding decrease in shear velocity estimate transition between background and inclusion material albeit
with an increase in estimator noise. Overall, results demonstrate the ability to generate high contrast shear velocity
images using sonoelastographic techniques and detect millimeter-sized lesions.
The purpose of this research was to introduce and analyze a technique for enhancing elasticity image quality using
locally adaptive Gaussian filtering. To assess the performance of this filtering method for reconstructing images with
missing or degraded data, heterogeneous images were simulated with circular regions of intensity twice that of the
surrounding material. Missing pixel data was introduced by thresholding a uniformly distributed noise matrix. Results
demonstrate locally adaptive Gaussian filtering accurately reconstructs the original image while preserving boundary
detail. To further analyze the performance of this filtering technique, multiple local image regions were suppressed and
normally distributed noise superimposed. Consequently, locally adaptive Gaussian filtering is capable of reconstructing
local missing data whereas both median and conventional Gaussian filtering fails. Using compressional elastographic
experimental data, results illustrate that locally adaptive Gaussian filtering is capable of minimizing decorrelation noise
artifacts while preserving lesion boundaries. Additionally, results obtained using vibrational shear velocity
sonoelastography further illustrate the ability of locally adaptive Gaussian filtering to enhance image quality by
minimizing estimator noise degradation in comparison to conventional spatial filtering techniques. Overall, results
indicate the feasibility of employing this spatial filtering technique for improving elasticity image quality while
preserving lesion boundaries.
Ultrasound-induced blood stasis was demonstrated thirty years ago. Most of the literature has been focused on methods employed to prevent stasis from occurring during ultrasound imaging. The current work discusses some of the theory behind this phenomenon. It also demonstrates ultrasound-induced blood stasis in murine tumor and muscle tissue, observed through noninvasive measurements of optical spectroscopy, and discusses possible diagnostic uses. We demonstrate that, using optical spectroscopy, effects of ultrasound can be used to noninvasively differentiate tumor from muscle tissue in mice, and that we can quantitatively differentiate tumor from muscle with maximum specificity 0.83, maximum sensitivity 0.79, and area under ROC curve 0.90, using a simple algorithm.
KEYWORDS: Magnetic resonance imaging, Data modeling, Tissues, Head, Optical filters, Medical imaging, Software development, Matrices, Data processing, Surgery
A method for fully automating the measurement of various neurological structures in MRI is presented. This technique uses an atlas-based trained maximum likelihood classifier. The classifier requires a map of prior probabilities, which is obtained by registering a large number of previously classified data sets to the atlas and calculating the resulting probability that each represented tissue type or structure will appear at each voxel in the data set. Classification is then carried out using the standard maximum likelihood discriminant function, assuming normal statistics. The results of this classification process can be used in three ways, depending on the type of structure that is being detected or measured. In the most straightforward case, measurement of a normal neural sub-structure such as the hippocampus, the results of the classifier provide a localization point for the initiation of a deformable template model, which is then optimized with respect to the original data. The detection and measurement of abnormal structures, such as white matter lesions in multiple sclerosis patients, requires a slightly different approach. Lesions are detected through the application of a spectral matched filter to areas identified by the classifier as white matter. Finally, detection of unknown abnormalities can be accomplished through anomaly detection.
The William Blake Archive is part of an emerging class of electronic projects in the humanities that may be described as hypermedia archives. It provides structured access to high-quality electronic reproductions of rare and often unique primary source materials, in this case the work of poet and painter William Blake. Due to the extensive high frequency content of Blake's paintings (namely, colored engravings), they are not suitable for very efficient compression that meets both rate and distortion criteria at the same time. Resolving that problem, the authors utilized modified Mixed Raster Content (MRC) compression scheme -- originally developed for compression of compound documents -- for the compression of colored engravings. In this paper, for the first time, we have been able to demonstrate the successful use of the MRC compression approach for the compression of colored, engraved images. Additional, but not less important benefits of the MRC image representation for Blake scholars are presented: because the applied segmentation method can essentially lift the color overlay of an impression, it provides the student of Blake the unique opportunity to recreate the underlying copperplate image, model the artist's coloring process, and study them separately.
Accurate computation of the thickness of articular cartilage in 3D is crucial in diagnosis of joint diseases. The purpose of this research project is to develop an unsupervised method to produce three-dimensional (3D) thickness map of articular cartilage with magnetic resonance imaging (MRI). The method consists of two main parts, cartilage extraction and thickness map computation. The initial segmentation for cartilage extraction is achieved using a recently proposed algorithm which depends on region-growing. The regions produced during this process are labeled as cartilage or non-cartilage using a voting procedure which essentially depends on local 2-class clustering and makes use of prior knowledge about cartilage regions. Following cartilage extraction, femoral and tibial cartilages are separated by detecting the interface between them using a deformable model. After the separation, the cartilage surfaces are reconstructed as a triangular mesh and divided into two plates according to the relation between surface normal at each vertex and principal axes of the structure. For surface reconstruction, we propose an algorithm which incorporates a simple MR imaging model which allows surface representations with sub-voxel accuracy. Our thickness computation algorithm treats each plate separately as a deformable model while considering the other plate as the target surface towards which it is deformed. At the end of deformation, the thickness values at each vertex is defined as the distance between the locations at pre and post-deformation instances. The performance of the cartilage segmentation is compared to manual tracing. Also, the performance evaluation of the thickness computation algorithm on phantoms resulted in RMS errors on the order of 1%.
KEYWORDS: Tissues, Liver, 3D image processing, 3D modeling, Image segmentation, Tumors, Doppler effect, In vitro testing, Ultrasonography, 3D metrology
Sonoelastography is a vibration Doppler technique for imaging the relative elasticity of tissues. Detectability of hard lesions of various sizes has previously been demonstrated in tissue phantoms by our group. Because real tissue differs from phantom material, the injection of formaldehyde in fresh liver tissue is being used as an in-vitro lesion model. Pieces of fresh calf liver were embedded in an agar gel then injected with a bolus of 37% formaldehyde to create a stiff lesion. Two and three-dimensional sonoelastography and b-mode images were acquired. The lesions were visible in each sonoelastography image as a region of reduced vibration. After imaging, lesions were dissected and measured for size and volume. One 0.4 cc bolus injection of formaldehyde created a lesion with a volume of 10.3 cc in the sonoelastography image compared to 9.3 cc using fluid displacement of the dissected lesion. A 0.33 cc injection of formaldehyde lesion created a volume of 5 cc in the sonoelastography image compared to 4.4 cc using fluid displacement. Sonoelastography imaging techniques for imaging hard lesions in phantoms can be successfully extended to imaging formaldehyde induced lesions in real tissue.
This paper presents an algorithm for segmentation of computed radiography (CR) images of extremities into bone and soft tissue regions. The algorithm is a region-based one in which the regions are constructed using a growing procedure with two different statistical tests. Following the growing process, tissue classification procedure is employed. The purpose of the classification is to label each region as either bone or soft tissue. This binary classification goal is achieved by using a voting procedure that consists of clustering of regions in each neighborhood system into two classes. The voting procedure provides a crucial compromise between local and global analysis of the image, which is necessary due to strong exposure variations seen on the imaging plate. Also, the existence of regions whose size is large enough such that exposure variations can be observed through them makes it necessary to use overlapping blocks during the classification. After the classification step, resulting bone and soft tissue regions are refined by fitting a 2nd order surface to each tissue, and reevaluating the label of each region according to the distance between the region and surfaces. The performance of the algorithm is tested on a variety of extremity images using manually segmented images as gold standard. The experiments showed that our algorithm provided a bone boundary with an average area overlap of 90% compared to the gold standard.
An algorithm for the reconstructions of ISO-resolution volumetric MR data sets from two standard orthogonal MR scans having anisotropic resolution has been developed. The reconstruction algorithm starts by registering a pair of orthogonal volumetric MR data sets. The registration is done by maximizing the correlation between the gradient magnitude using a simple translation-rotation model in a multi-resolution approach. Then algorithm assumes that the individual voxels on the MR data are an average of the magnetic resonance properties of an elongated imaging volume. Then, the process is modeled as the projection of MR properties into a single sensor. This model allows the derivation of a set of linear equations that can be used to recover the MR properties of every single voxel in the SO-resolution volume given only two orthogonal MR scans. Projections on convex sets (POCS) was used to solve the set of linear equations. Experimental results show the advantage of having a ISO-resolution reconstructions for the visualization and analysis of small and thin muscular structures.
Halftones are intended to produce the illusion of continuous images form binary output states, so the visibility of undesired halftone textures is an essential quality factor of halftone patterns. We propose a metric to predict the visibility of color halftone textures. The metric utilizes the human visual threshold function and contrast sensitivity functions of luminance and chrominance. The threshold is related to the average background luminance level by de Vries-Rose law. An iterative approach was used to determine the distance in which the visual error just exceeds the visual threshold. This distance is the metric that predicts the critical distance that a human observer can just discriminate the textures. To verify the metric, the texture visibility was determined experimentally by a psychological experiment. The halftone stimuli were presented on an SGI monitor. Starting from an initial distance, where the halftone images appeared as continuous color patches, the subject walked toward the monitor and found the distance where he or she could just discriminate the spatial changes caused by the textures. Then the distances determined by the experiment and those predicted by the metric were compared. A good correlation was achieved. The results show that the metric is able to predict the visibility over a wide range of texture characteristics.
KEYWORDS: Signal to noise ratio, Ultrasonography, Signal processing, Doppler effect, Transducers, Interference (communication), Signal generators, In vitro testing, Optical filters, Electronic filtering
The effect of signal decorrelation on the performance of the Butterfly Search velocity estimator is examined. An analytical approximation for the expected value of the Butterfly Search L(v) function is developed for three cases of interest. The approximations are verified against synthesized echo data. It is found that the peak value of the L(v) function is limited by the rate of signal decorrelation. The results show that improved performance may be obtained by processing and averaging subsets of echo ensembles, rather than applying the Butterfly Search to the entire ensemble simultaneously. For lower SNRs, processing the entire ensemble at once produces equivalent or better results than subset processing. Results from echo data obtained in-vitro are presented which confirm the simulations.
It has been found that the L* function defined in the CIELAB color space is not suitable to predict the human visual perception of modulated patterns at high spatial frequencies. For example, in multilevel halftoning (multitoning), when output levels are equally spaced in L*, it has been observed that the visibility of the resulting multitone patterns is not uniform across different parts of the tone scale. This leads to the hypothesis that the CIE L* function may not be a good metric to evaluate the perceived lightness differences at high-spatial frequencies as it was derived based on the perception of large area uniform patches. To investigate the relationship between suprathreshold lightness difference perception with regard to spatial frequency and amplitude modulation, we designed a psychophysical experiment, which was conducted using a lightness difference matching paradigm. The stimuli used in the experiment were horizontal square-wave gratings. The behavior of lightness difference perception under varying spatial frequencies and modulation amplitudes across the entire L* scale was studied. Consistent results were acquired that show a significant frequency-dependent effect where the effective lightness difference for high- frequency patterns is reduced for low L* values. The magnitude of this effect was found to be highly related to the spatial frequency of the modulation. Based on these results, we derived an effective lightness function that is dependent on spatial frequency. The effective lightness function can be applied to the selection of the output levels for multitoning.
This paper describes the characterization of a unique thin- film ultrasound phantom. The phantom consist of a film with controllable acoustic properties immersed in an ultrasonically transparent material. The placement of scattering sites on the film creates an image when scanned with a clinical instrument.
This work presents a reliable automatic segmentation algorithm for multispectral MRI data sets. We propose the use of an automatic statistical region growing algorithm based on a robust estimation of local region mean and variance for every voxel on the image. The best region growing parameters are automatically found via the minimization of a cost functional. Furthermore, we propose a hierarchical use of relaxation labeling, region splitting, and constrained region merging to improve the quality of the MRI segmentation. We applied this approach to the segmentation of MRI images of anatomically complex structures which suffer signal fading and noise degradations.
In this work we present a comprehensive approach for the kinematic analysis of musculoskeletal structures based on 4D MRI data sets and unsupervised segmentation. We applied this approach to the kinematics analysis of the knee flexion. The unsupervised segmentation algorithm automatically detects the number of spatially independent structures present in the medical image. The motion tracking algorithm is able to pass simultaneously the segmentation of all the structures which allows an automatic segmentation and tracking of the soft tissue and bone structures of knee in a series of volumetric images. Our approach requires a minimum of interactivity with the user, eliminating the need for exhaustive tracings and editing of image data. This segmentation approach allowed us to visualize and analyze the 3D knee flexion, and the local kinematics of the meniscus.
KEYWORDS: Chemical elements, Motion estimation, Tissues, Ultrasonography, Speckle pattern, Speckle, Finite element methods, Ultrasonics, Detection and tracking algorithms, Medical imaging
By exploiting the correlation of ultrasound speckle patterns that result from scattering by underlying tissue elements, 2D tissue motion can be theoretically recovered by tracking the apparent movement of the speckle patterns. Speckle tracking, however, is an ill-posed inverse problem because of temporal decorrelation of the speckle patterns and the inherent low signal-to-noise ratio of medical ultrasonic images. This paper investigates the use of an adaptive deformable mesh for non-rigid tissue motion recovery from ultrasound images. The nodes connecting the mesh elements are allocated adaptively to stable speckle patterns that are less susceptible to temporal decorrelation. We use the approach of finite element analysis in manipulating the irregular mesh elements. A novel deformable block matching algorithm, making use of a Lagrange element for higher-order description of local motions, is proposed to estimate a non- rigid motion vector at each node. In order to ensure that the motion estimates are admissible to a physically plausible solution, the nodal displacements are regularized by minimizing the strain energy of the mesh deformations. Experiments based on ultrasound images of muscle contraction and on computer simulations have shown that the proposed algorithm can successfully track non-rigid displacement fields.
Multilevel halftoning (multitoning) is an extension of bitonal halftoning, in which the appearance of intermediate tones is created by the spatial modulation of more than two tones, i.e., black, white, and one or more shades of gray. In this paper, a conventional multitoning approach and a specific approach, both using stochastic screen dithering, are investigated. Typically, a human visual model is employed to measure the perceived halftone error for both algorithms. We compare the performance of each algorithm at gray levels near the intermediate printer output levels. Based on this study, an over-modulation algorithm is proposed. This algorithm requires little additional computation and the halftone output is mean-preserving with respect to the input. We will show that, with this simple over-modulation scheme, we will be able to manipulate the dot patterns around the intermediate output levels to achieve desired halftone patterns. Investigation on optimal output level selection and inkjet printing simulation for this new scheme will also be reported.
The blue noise mask (BNM) is a halftone screen that produces unstructured visually pleasing dot patterns. The BNM combines the blue-noise characteristics of error diffusion and the simplicity of ordered dither. A BNM is constructed by designing a set of interdependent binary patterns for individual gray levels. In this paper, we investigate the quality issues in blue-noise binary pattern design and mask generation as well as in application to color reproduction. Using a global filtering technique and a local 'force' process for rearranging black and white pixels, we are able to generate a series of binary patterns, all representing a certain gray level, ranging from white-noise pattern to highly structured pattern. The quality of these individual patterns are studied in terms of low-frequency structure and graininess. Typically, the low-frequency structure (LF) is identified with a measurement of the energy around dc in the spatial frequency domain, while the graininess is quantified by a measurement of the average minimum distance (AMD) between minority dots as well as the kurtosis of the local kurtosis distribution (KLK) for minority pixels of the binary pattern. A set of partial BNMs are generated by using the different patterns as unique starting 'seeds.' In this way, we are able to study the quality of binary patterns over a range of gray levels. We observe that the optimality of a binary pattern for mask generation is related to its own quality mertirc values as well as the transition smoothness of those quality metric values over neighboring levels. Several schemes have been developed to apply blue-noise halftoning to color reproduction. Different schemes generate halftone patterns with different textures. In a previous paper, a human visual system (HVS) model was used to study the color halftone quality in terms of luminance and chrominance error in CIELAB color space. In this paper, a new series of psycho-visual experiments address the 'preferred' color rendering among four different blue noise halftoning schemes. The experimental results will be interpreted with respect to the proposed halftone quality metrics.
The ordered color filter arrays (CFA) used in single sensor, color digital still cameras introduce distracting color artifacts. These artifacts are due to the phase shifted, aliased signals introduced by the sparse sampling by the CFAs. This work reports the results of an investigation on the possibility of using random patterns as a CFA for single sensor, digital still cameras. From a single blue noise mask pattern, three mutually exclusive, random CFAs are constructed representing the red, green, and blue color filters. An edge adaptive method consisting of missing-pixel edge detection and boundary sensitive interpolation is employed to reconstruct the entire image. Experiments have shown that the random CFA alleviates the problem of the low-frequency color banding associated with ordered arrays. This method also has the advantage of better preserving color free, sharp neutral edges, and results in less deviation from neutral on high frequency, monochrome information.
A novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis is presented. On wavelet decomposition applied to a given mammographic image, we integrate the information of the tree-structured zero crossings of wavelet coefficients and the information of the low-pass-filtered subimage to enhance the desired image features. A discrete wavelet transform with pyramidal structure is employed to speedup the computation for wavelet decomposition and reconstruction. The spatiofrequency localization property of the wavelet transform is exploited based on the spatial coherence of image and the principle of human psychovisual mechanism. Preliminary results show that the proposed approach is able to adaptively enhance local edge features, suppress noise, and improve global visualization of mammographic image features. This wavelet-based multiresolution analysis is therefore promising for computerized mass screening of mammograms.
Mammographic images are often of relatively low contrast and poor sharpness with non-stationary background or clutter and are usually corrupted by noise. In this paper, we propose a new method for microcalcification detection using gray scale morphological filtering followed by multiresolution fusion and present a unified general filtering form called the local operating transformation for whitening filtering and adaptive thresholding. The gray scale morphological filters are used to remove all large areas that are considered as non-stationary background or clutter variations, i.e., to prewhiten images. The multiresolution fusion decision is based on matched filter theory. In addition to the normal matched filter, the Laplacian matched filter which is directly related through the wavelet transforms to multiresolution analysis is exploited for microcalcification feature detection. At the multiresolution fusion stage, the region growing techniques are used in each resolution level. The parent-child relations between resolution levels are adopted to make final detection decision. FROC is computed from test on the Nijmegen database.
Color halftoning using a conventional screen requires careful selection of screen angles to avoid Moire patterns. An obvious advantage of halftoning using a blue noise mask (BNM) is that there are no conventional screen angle or Moire patterns produced. However, a simple strategy of employing the same BNM on all color planes is unacceptable in case where a small registration error can cause objectionable color shifts. In a previous paper by Yao and Parker, strategies were presented for shifting or inverting the BNM as well as using mutually exclusive BNMs for different color planes. In this paper, the above schemes will be studied in CIE-LAB color space in terms of root mean square error and variance for luminance channel and chrominance channel respectively. We will demonstrate that the dot-on-dot scheme results in minimum chrominance error, but maximum luminance error and the 4-mask scheme results in minimum luminance error but maximum chrominance error, while the shift scheme falls in between. Based on this study, we proposed a new adaptive color halftoning algorithm that takes colorimetric color reproduction into account by applying 2-mutually exclusive BNMs on two different color planes and applying an adaptive scheme on other planes to reduce color error. We will show that by having one adaptive color channel, we obtain increased flexibility to manipulate the output so as to reduce colorimetric error while permitting customization to specific printing hardware.
We propose a novel segmentation algorithm called SMART for color, complex documents. It decomposes a document image into 'binarizable' and 'non-binarizable' components. The segmentation procedure includes color transformation, halftone texture suppression, subdivision of the image into 8 by 8 blocks, classification of the 8 by 8 blocks as 'active' or 'inactive', formation of macroblocks from the active blocks, and classification of the macroblocks as binarizable or non-binarizable. The classification processes involve the DCT coefficients and a histogram analysis. SMART is compared to three well-known segmentation algorithms: CRLA, RXYC, and SPACE. SMART can handle image components of various shapes, multiple backgrounds of different gray levels, different relative grayness of text to this background, tilted image components, and text of different gray levels. To compress the segmented image, we apply JPEG4 to the non-binarizable macroblocks and the Group 4 coding scheme to the binary image representing the binarizable macroblocks and to the bitmap storing the configuration of all macroblocks. Data about the representative gray values, the color information, and other descriptors of the binarizable macroblocks and the background regions are also sent to allow image reconstruction. The gain is using our compression algorithm over using JPEG for the whole image is significant. This gain increases as the proportion of the size of the subjects prefer the reconstructed images from our compression algorithm to those form the bitrate-matching JPEG images. In a series of test images, this document segmentation and compression system enables compression ratios two times to six times improved over standard methods.
In this paper we present a local force model and its integration in a hierarchical analysis of the estimation of the left ventricle motion over a cardiac cycle. The local force model is derived from the dynamics of independent point masses driven by local constant forces over a short time. A force field is assumed to be constant over short periods of time. This force drives independent point masses within a regional patch of the left ventricle surface from one time instant to another. The trajectory that minimizes the energy required to move the point mass from one surface to another is considered as the local displacement vector. This estimated trajectory takes into account surface constraints and previous estimations derived from the volumetric image sequences so that the point masses travel along smooth trajectories resembling the realistic left ventricle surface dynamics. This proposed model is able to recover the point correspondence of the nonrigid motions between consecutive frames when the surfaces and the initial conditions of left ventricle at consecutive time frames are given. The local force model is incorporated in a hierarchical analysis scheme providing us with the complete dynamics of the left ventricle as compared to the local kinematic analysis of previous approaches. Experimental results based on synthetic and real left ventricle CT volumetric images show that the proposed scheme is very promising for cardiac analysis.
In this paper, we propose a Laplacian matched filter based approach for small object detection using gray scale morphological filtering combined with wavelet-based multiresolution analysis. This multiresolution matched filter based detection includes two stages: prewhitening processing and matched filter detection fusion. The gray scale morphological filters are used as prewhitening filters. The wavelet transform relates directly the Laplacian matched filters with multiresolution analysis. Preliminary tests of a small object detection on simulated narrow band clutter and microcalcification detection from mammographic images show that the proposed approach is capable of a tool for small object detection without explicit assumptions about image background and noise statistics. A general form for whitening filtering and adaptive thresholding unified as the local operation transformation (LOT) is also presented.
We propose a joint source/channel coding scheme to transmit image through binary noisy channels based on 2-D discrete cosine transform (DCT) and trellis coded quantization (TCQ). When an image is transmitted through noisy channel with high throughput, both image compression and error-resilient coding scheme need to be considered. After the discrete cosine transform, the source image is decomposed into several subsources according to the transform coefficient positions, i.e., the same frequency coefficients in different DCT blocks are grouped together as a single subsource. The mean and variance values are used to construct the scalar codebooks for TCQ. Uniform threshold trellis coded quantizer is constructed to release the complexity and the transform coefficients are quantized by these fixed-rate quantizer and transmitted through noisy channels. No explicit error protection is used. The steepest descent method and iterative schemes are employed to determine the optimum bit allocation among the subsources subject to the constraints of the average coding rate and allowable maximum bits to each sample. Neighborhood relation is employed to limit the searching space when a bit is to be allocated to certain subsource. Simulation results show that the performance is very promising.
The conventional method for sending halftone images
via facsimile machines is inefficient. The previously proposed Tone-Fac algorithm improves the transmission of halftone images. Tone- Fac represents a halftone image by mean gray values of the disjoint blocks and an error image, which records the difference between the desired halftone and the halftone generated using the mean gray values. To improve on ToneFac, we propose additional processing techniques: searching for the error-minimizing gray value for each block; quantization and coding of block values; bit switching, which transforms the error image into a more compressible image; optimal block sizing; and spurious dot filtering, which removes perceptually insignificant dots. The new algorithm is compared to
other methods, including adaptive arithmetic coding, and is shown to provide improvement in bit rate. A theoretical consideration of the compression ratio from the ToneFac algorithm is also given.
We propose in this paper a variable-coefficient fixed-length (VCFL) coding scheme for wavelet-based image transmission over noisy channels. When an image is transmitted through noisy channel with high throughput, both image compression and error-resistant coding scheme need to be considered. In this approach, an image is first decomposed into subbands by wavelet transform and quantized using an adaptive quantization scheme. The adaptive quantization is adaptive to both the frequency characteristics and the spatial constraints based on Gibbs random field. The traditional variable length entropy coding schemes, such as Huffman coding or arithmetic coding, and the fixed length coding such as LZW are usually very sensitive to channel noise for image transmission applications. Even with the insertion of synchronization symbols, they still cannot be directly employed without additional error correction/detection coding. To overcome the difficulty of image transmission over noisy channels, we propose to code the quantized subband coefficients with the VCFL scheme. This coding scheme attempts to keep the balance between redundancy removal, synchronization detection and error resilience. Part of the codebook is field based on the observation of the coefficient spatial distribution patterns in each subbands to alleviate the transmission of the codebook. The remaining code positions within the fixed length codebook can be utilized to combat channel errors by carefully arranging the code positions such that the codes with biggest transition cost will have the biggest Hamming distance. These positions can laos be filled with other frequently appeared coefficient composition sequences to achieve higher compression ratio. Experimental results of image transmission over noisy channels are reported to show the promising potential of the proposed coding scheme.
This paper presents a novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis. Upon wavelet decomposition applied to a given mammographic image, we integrate the information of the tree-structured zerocrossings of wavelet coefficients and the information of the low-pass filtered subimage to enhance the desired image features. A discrete wavelet transform with pyramidal structure has been employed to speed up the computation for wavelet decomposition and reconstruction. The spatio-frequency localization property of the wavelet transform is exploited based on the spatial coherence of image and the principle of human psychovisual mechanism. Preliminary results show that the proposed approach is able to adaptively enhance local edge features, suppress noise, and improve global visualization of mammographic image features. This wavelet-based multiresolution analysis is therefore promising for computerized mass screening of mammograms.
In wireless image communication, image compression is necessary because of the limited channel bandwidth. The associated channel fading, multipath distortion and various channel noises demand that the applicable image compression technique be amenable to noise combating and error correction techniques designed for wireless communication environment. In this study, we adopt a wavelet-based compression scheme for wireless image communication applications. The scheme includes a novel scene adaptive and signal adaptive quantization which results in coherent scene representation. Such representation can be integrated with the inherent layered structure of the wavelet-based approach to provide possibilities for robust protection of bit stream against impulsive and bursty error conditions frequently encountered in wireless communications. To implement the simulation of wireless image communication, we suggest a scheme of error sources modeling based on the analysis of the general characteristics of the wireless channels. This error source model is based on Markov chain process and is used to generate binary bit error patterns to simulate the bursty nature of the wireless channel errors. Once the compressed image bit stream is passed through the simulated channel, errors will occur according to this bit error pattern. Preliminary comparison between JPEG-based wireless image communication and wavelet-based wireless image communication has been made without application of error control and error resilience to either case. The assessment of the performance based on image quality evaluation shows that the wavelet-based approach is promising for wireless communication with the bursty channel characteristics.
Compression of 3D or 4D medical image data has now become imperative for clinical picture archiving and communication systems (PACS), telemedicine and telepresence networks. While lossless compression is often desired, lossy compression techniques are gaining acceptance for medical applications, provided that clinically important information can be preserved in the coding process. We present a comprehensive study of volumetric image compression with three-dimensional wavelet transform, adaptive quantization with 3D spatial constraints, and octave zerotree coding. The volumetric image data is first decomposed using 3D separable wavelet filterbanks. In this study, we adopt a 3-level decomposition to form a 22-band multiresolution pyramid of octree. An adaptive quantization with 3D spatial constraints is then applied to reduce the statistical and psychovisual redundancies in the subbands. Finally, to exploit the dependencies among the quantized subband coefficients resulting from 3D wavelet decomposition, an octave zerotree coding scheme is developed. The proposed volumetric image compression scheme is applied to a set of real CT medical data. Significant coding gain has been achieved that demonstrates the effectiveness of the proposed volumetric image compression scheme for medical as well as other applications.
KEYWORDS: Reconstruction algorithms, Feature extraction, 3D modeling, Data modeling, 3D metrology, Natural surfaces, Image segmentation, Heart, 3D scanning, Ultrasonography
In this paper, a novel technique is presented for the extraction of features from 3D medical image sequences. This technique involves grayscale segmentation, followed by application of a 3D deformable model algorithm which smooths the data and compensates for drop-out regions in the segmentation. These properties are particularly desirable in the application studied here, which is the extraction of the left ventricle from a 3D ultrasound scan. THe algorithm is shown to produce a good reconstruction of the LV, as well as an accurate measurement of its volume.
A scanned, complex document image may be composed of text, graphics, halftones, and pictures, whose layout is unknown. In this paper, we propose a novel segmentation scheme for scanned document images that facilitates their efficient compression. Our scheme segments an input image into binarizable components and no-binarizable components. By a binarizable component we mean that the region can be represented by no more than two gray levels (or colors) with acceptable perceptual quality. A non-binarizable component is defined as region that has to be represented by more than two gray levels (or colors) with acceptable perceptual quality. Once the components are identified, the binarizable components can be thresholded and compressed as a binary image using an efficient binary encoding scheme together with the gray values represented by the black and white pixels of the binary image. The non-binarizable components can be compressed using another suitable encoding scheme.
Image interpolation is the determination of unknown pixels based on some known pixels. The conventional interpolation methods such as pixel replication, bilinear interpolation, and cubic spline interpolation, assume that the known pixels are located regularly on a Cartesian mesh. They cannot be easily extended to other cases where the configurations of the known pixels are different. We propose a novel formulation of the image interpolation problem to deal with the more general cases, such as the case where a region of image is missing and the case where the known pixels are irregularly placed. The interpolation problem is formulated into a boundary value problem involving the Laplacian equation and the known pixels as the boundary conditions. The matrix equation resulting from the formulation has a unique solution. It can be solved efficiently by the successive over-relaxation (SOR) iteration. The advantage of the proposed interpolation method lies in its flexibility in handling the general cases of interpolation.
We describe in this paper a novel cellular connectionist neural network model for the implementation of clustering-based Bayesian image segmentation with Gibbs random field spatial constraints. The success of such an algorithm is largely due to the neighborhood constraints modeled by the Gibbs random field. However, the iterative enforcement of the neighborhood constraints involved in the Bayesian estimation would generally need tremendous computational power. Such computational requirement hinders the real-time application of the Bayesian image segmentation algorithms. The cellular connectionist model proposed in this paper aims at implementing the Bayesian image segmentation with real-time processing potentials. With a cellular neural network architecture mapped onto the image spatial domain, the powerful Gibbs spatial constraints are realized through the interactions among neurons connected through their spatial cellular layout. This network model is structurally similar to the conventional cellular network. However, in this new cellular model, the processing elements designed within the connectionist network are functionally more versatile in order to meet the challenging needs of Bayesian image segmentation based on Gibbs random field. We prove that this cellular neural network does converge to the desired steady state with a properly designed update scheme. An example of CT volumetric medical image segmentation is presented to demonstrate the potential of this cellular neural network for a specific image segmentation application.
We present in this paper a scheme to analyze the left ventricle motion over a cardiac cycle through the integration of the hierarchical surface fitting and the point correspondence estimation. The hierarchical surface fitting is a coarse-to-fine analysis scheme and has been successfully applied to cine-angiographic cardiac images. In this study, the hierarchical surface fitting and motion analysis is applied to a set of CT images with real volumetric nature. We also incorporate an additional global deformation, long axis bending, into the shape model to reflect the curved nature of the left ventricle long axis. With the dense volumetric data, we are able to implement higher order spherical harmonics in the analysis of the local deformations. The fitted surface allows us a complete recovery of the Gaussian curvature of the shape. The estimation of the point correspondence is accomplished through the analysis of the first fundamental form and the Gaussian curvature computed from the fitted shape assuming conformal motion. The overall coarse-to-fine hierarchical analysis and the parametric nature of the fitted surface enable us to compute the Gaussian curvature analytically and gain a clear and complete description of the left ventricle dynamics based on the shape evolution over the cardiac cycle. Results based on a set of CT data of 16 volumes show that this hierarchical surface fitting and motion analysis scheme is promising for cardiac analysis.
Color halftoning using a conventional screen requires rotating the screen by different angles for different color planes to avoid Moire patterns. An obvious advantage of halftoning using a blue noise mask (BNM) is that there are no screen angles or Moire patterns. However, a simple strategy of employing the same BNM on all color planes is unacceptable in cases where a small registration error can cause objectionable color shifts. In a previous paper, we proposed shifting or inverting the BNM for different color planes. The shifting technique can, at certain shift values, introduce low frequency contents into the halftone image, whereas the inverting technique can be used only on two color planes. In this paper, we propose a technique that uses four distinct BNMs that are correlated in a way such that the low frequency noise resulting from the interaction between the BNMs is significantly reduced.
The blue noise mask (BNM) is a stochastic screen that produces visually pleasing blue noise. In its construction, a filter is applied to a given dot pattern to identify clumps in order to add or remove dots and thereby generate a correlated binary pattern for the next level. But up to now, all the filters were selected on a qualitative basis. There is no reported work describing precisely how the filtering and selection of dots affects the perceived error of the binary pattern. In this paper, we give a strict mathematical analysis of the BNM construction based on a human visual model, which provides insights to the filtering process and also prescribes the locations of the dots that will result in a binary pattern of minimum perceived error when swapped. The analysis also resolves some unexplained issues noticed by other researchers.
This work considers the use of digital halftones in the display of medical images. One might assume that the use of halftone rendering (as opposed to continuous tone image rendering) will degrade the information in medical images, therefore, it is interesting to study what degree of degradation is unacceptable in medical images. We analyze various halftoning techniques quantitatively by first generating low-contrast detail diagrams (CDD) made to represent computed tomography (CT), magnetic resonance (MR), and ultrasound (US) modality images. These are then halftoned and printed using error diffusion, Bayer's method, blue noise mask, and centered weighted dots. The contrast areas in the diagram are randomly placed on a 5 X 5 grid. A single observer is used to determine the minimum contrast `lesion' that could be observed. The results for minimum detectable contrast depend on resolution (dots per inch), modality, and halftoning technique. It is shown that acceptable halftone rendering, with small degradation, can be achieved under certain conditions.
We present in this paper a study of medical image compression based on an adaptive quantization scheme capable of preserving clinically useful structures appeared in the given images. We believe that how accurate can a compression algorithm preserve these structures is a good measure of image quality after compression since many image-based diagnoses are based on the position and appearance of certain structures. With wavelet decomposition, we are able to investigate the image features at different scale levels that correspond to certain characteristics of biomedical structures contained in the medical images. An adaptive quantization algorithm based on clustering with spatial constraints is then applied to the high frequency subbands. The adaptive quantization enables us to selectively preserve the image features at various scales so that desired details of clinically useful structure are preserved during the process of compression, even at a low bit rate. Preliminary results based on real medical images suggest that this clustering-based adaptive quantization, combined with wavelet decomposition, is very promising for medical image compression with structure-preserving capability.
Frequency modulated (FM) halftoning or 'stochastic screening,' has attracted a great deal of attention in the printing industry in recent years. It has several advantages over conventional halftoning. But one serious problem that arises in FM halftoning is dot gain. One approach to stochastic screening uses a specially constructed halftone screen, the blue noise mask (BNM), to produce an unstructured and visually appealing pattern of halftone dots at any gray level. In this paper, we will present methods to correct dot gain with the BNM. Dot gain is related to the area-to-perimeter ration of printed spots. We can exploit this feature in different ways. At a medium level, a B>NM pattern will have 'connected' as well as 'isolated' dots. Normally, as we build down BNM patterns to lower levels, a specific number of white dots will be replace by black dots. Since connected white dots are more likely to be picked than isolated white dots, this will results in substantial dot gain because of the increasing number of isolated white dots. We show that it is possible to constrain the process of constructing a BNM such that isolated dots are preferentially removes, thus significantly reducing dot gain in a BNM.
The rate-distortion trade-off in the discrete cosine transform- based coding scheme in ISO/JPEG is determined by the quantization table. To permit a different quality to be selected by a user, a common practice is to scale the standard quantization tables that have been empirically determined from psychovisual experiments. In this paper, an algorithm is presented to generate a quantization table that is optimized for a given image and for a given distortion. The computationalcomplexity of this algorithm is reduced compared to other techniques. The optimized, image-adaptive quantization table
typically yields an improvement of 15% to 20% in bit rate compared to the use of standard, scaled quantization tables. Once an optimized quantization table has been generated for a specific image,
it can also be applied to other images with similar content with a small sacrifice in bit rate.
The Gibbs random field (GRF) has proved to be a simple
and practical way of parameterizing the Markov random field, which has been widely used to model an image or image-related process in many image processing applications. In particular, the GRF can be employed to construct an efficient Bayesian estimation that often yields optimal results. We describe how the GRF can be efficiently incorporated into optimization processes in several representative applications, ranging from image segmentation to image enhancement. One example is the segmentation of computerized tomography (CT) volumetric image sequence in which the GRF has been incorporated into K-means clustering to enforce the neighborhood constraints. Another example is the artifact removal in discrete cosine
transform-based low bit rate image compression where GRF has been used to design an enhancement algorithm that reduces the "blocking effect" and the 'Wnging effect" while still preserving the image details. The third example is the integration of GRF in a wavelet-based subband video coding scheme in which the highfrequency subbands are segmented and quantized with spatial constraints specified by a GRF, and the subsequent enhancement of the decompressed images is accomplished by smoothing with another type of GRF. With these diverse examples, we are able to demonstrate that various features of images can all be properly characterized by a GRF. The specific form of the GRF can be selected according to the characteristics of an individual application. We believe that the GRF is a powerful tool to exploit the spatial dependency in various images, and is applicable to many image processing tasks.
The conventional method for sending halftone images via facsimile machines is inefficient. The ToneFac algorithm previously proposed improves the efficiency of halftone image transmission. ToneFac represents a halftone image by mean block values and an error image. To improve on ToneFac, this paper proposes additional processing techniques: searching for the error-minimizing gray value for each block, quantization and coding of block values, bit switching, which transforms the error image into a more compressible image, optimal block sizing, and spurious dot filtering, which removes perceptually insignificant dots. The new algorithm is compared to other methods including adaptive arithmetic coding, and is shown to provide improvement in bit rate.
We present in this paper a study of subband analysis and synthesis of volumetric medical images using 3D separable wavelet transforms. With 3D wavelet decomposition, we are able to investigate the image features at different scale levels that correspond to certain characteristics of biomedical structures contained in the volumetric images. The volumetric medical images are decomposed using 3D wavelet transforms to form a multi-resolution pyramid of octree structure. We employ a 15-subband decomposition in this study, where band 1 represents the subsampled original volumetric images and other subbands represent various high frequency components of a given image. Using the available knowledge of the characteristics of various medical images, an adaptive quantization algorithm based on clustering with spatial constraints is developed. Such adaptive quantization enables us to represent the high frequency subbands at low bit rate without losing clinically useful information. The preliminary results of analysis and synthesis show that, by combining the wavelet decomposition with the adaptive quantization, the volumetric biomedical images can be coded at low bit rate while still preserving the desired details of biomedical structures.
The Gibbs random field (GRF) has been proved to be a simple and practical way of parameterizing the Markov random field which has been widely used to model an image or image related process in may image processing applications. In particular, Gibbs random field can be employed to construct an efficient Bayesian estimation that often yields optimal results. In this paper, we describe how the Gibbs random field can be efficiently incorporated into optimization processes in several representative applications, ranging from image segmentation to image enhancement. One example is the segmentation of CT volumetric image sequence in which the GRF has been incorporated into K-means clustering to enforce the neighborhood constraints. Another example is the artifact removal in DCT based low bit rate image compression in which GRF has been used to design an enhancement algorithm that smooths the artificial block boundary as well as ringing pattern while still preserve the image details. The third example is an elegant integration of GRF into a wavelet subband coding of video signals in which the high-frequency bands are segmented with spatial constraints specified by a GRF while the subsequent enhancement of the decompressed images is accomplished with the smoothing function specified by another corresponding GRF. With these diverse examples, we are able to demonstrated that various features of images can be all properly characterized by a Gibbs random field. The specific form of the Gibbs random field can be selected according to the characteristics of an individual application. We believe that Gibbs random field is a powerful tool to exploit the spatial dependency in various images, and is applicable to many other image processing tasks.
We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation. Results appear to provide improvements over previous techniques, in terms of both low-resolution detail and accuracy.
The blue noise mask (BNM) is a halftone screen that produces unstructured, visually pleasing halftone images. Since it is a point process, halftoning using the BNM can be implemented considerably faster than error diffusion and other algorithms. However, in the construction of the original BNM, a number of constraints were used to limit its characteristics in the spatial and frequency domains. These constraints were not efficient to compute and required adaptability to all gray levels in the construction process. The original BNM also contained some small but unwanted low-frequency components at some gray levels. In this paper, we present a revised approach to the generation of blue noise patterns and the construction of BNMs employing more efficient computations and eliminating more unwanted residual low-frequency components. Psychovisual evaluation shows that dithering with the new BNM gives excellent results and its rating is statistically indistinguishable from that of error diffusion with serpentine raster and perturbed weights.
We propose an algorithm in which a sequence of digital
halftone images is efficiently transmitted using encoding compatible with conventional facsimile devices. To enhance the CCITT standardized coding schemes for Group 3 and Group 4 facsimile apparatus,
pre-encoding is done on each image in the sequence: The image is either pre-encoded as a combination of bit representation of block means and an "error" image, according to the ToneFac algorithm, or it is pre-encoded as an "interimage" in which interframe
redundancy is converted into spatial redundancy, and the least "busy" of the above images is encoded and transmitted. The approach is referred to as the ToneSec algorithm.
Inverse haiftoning is the method by which an approximation of a gray-scale image is reconstructed from a binary, halftoned version of the original. Several inverse-halftone algorithms are described, including a three-level cascade algorithm. We demonstrate that a priori knowledge of the halftone technique is not essential, but can be used if available. Finaily, we demonstrate the results of applying inverse-halftone operations to both computer
synthesized and photographic images.
The efficient encoding and transmission of information for facsimile communication relies on redundancy in the scanned pixels. Halftone images, especiaily those rendered by high-quality dispersed
dot techniques, are "busy" with alternative black and white pixels and shorter run lengths as compared to text information. Because of this, it is desirable to increase the redundancy and decrease the entropy of those images for efficient encoding and transmission. We propose a novel technique whereby both transmiffing
and receiving fax devices have in memory a halftone screen such as the "blue noise mask" (BNM). The BNM is a halftone screen that produces a visuaily appealing dispersed dot pattern with an unstructured, isotropic pattern. When both the transmitting and receiving fax devices have the same halftone screen in ROM, the problem of halftone image encoding can be reduced to that of transmitting the mean gray value of blocks, or subimages, followed by a sparse halftone error image with increased redundancy and
run-lengths compared to the original halftone. Examples show that by using the proposed technique, image entropy can be reduced to 0.2 bits/pixel, and typical run-lengths can increase by a factor of
5. The increase in image quality, combined with increased transmission speed, could add considerably to the utilization and acceptance of halftone fax images.
We propose a new digital halftoning algorithm where the halftoning is achieved by a pixelwise comparison of the gray scale image against a nonimage array, the blue noise mask. The blue noise mask is constructed such that when thresholded at any level, the resulting binary pattern has the correct first order statistics, and also its power spectrum has blue noise (high frequency) characteristics which are visually pleasing. The construction of the blue noise mask is described and experimental results are shown. Also results from a phychovisual study are provided where subjects rated halftoned images that have the same first order but different second order statistics.
This paper presents a quick and efficient way to detect and correct the linear and constant image-phase terms associated with MR images. We show that this correction provides us with the knowledge of the exact location of the DC term in k-space, which proves to be useful in the detection of x and y motion parameters. In addition, by displaying the real positive part of the image after the proposed correction, we can reduce background noise, motion artifacts and flow artifacts. Examples, analyses and results are provided to demonstrate the usefulness of the proposed detection and correction method.
KEYWORDS: Motion models, Data modeling, Magnetic resonance imaging, Data acquisition, 3D modeling, Image processing, Mathematical modeling, 3D image processing, Fourier transforms, Computer programming
In this paper, we present a comprehensive model for MR data acquisition in the presence of patient motion to provide a better understanding as to the source of motion artifacts. This model identifies and quantifies various sources of motion artifacts in 2-D Fourier imaging. We verify our model by comparing the results predicted by the model with actual MR images of phantoms subjected to motion with controlled parameters. We expect that the knowledge of the sources of artifacts will lead to new and better methods of compensating for them.
Artifacts due to patient motion in the slice-selection direction (Z motion) have been a major source of MR image degradation for many years but have not been addressed as much as in-plane motion due to the complexity of modeling and correcting for the motion. In this paper we present a model and a detection and correction scheme for amplitude aberrations due to motion in the slice-selection direction. 1.
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