Polarimetric imaging data contains rich information about surface textures, shape, and shading of objects in a scene, which can be used to discriminate objects from background. Due to spectral signatures are limited to material properties, separating man-made objects from natural scene is a difficult task in complex scene. In this paper, we present a man-made object separation technique from natural scene utilizing polarimetric data. We started by calculating different polarimetric component images based on Stokes vector measurement. After that, combining polarimetric component images into color space and single-channel conversion, a polarimetric signature image is calculated. Then, considering pixel neighborhood relationship, an incremental clustering approach is applied to group similar pixel patterns of polarimetric signature image. Finally, a morphological structuring element is applied to conduct the morphological close operation to refine the final background mask. Ground truth is generated manually. A high-performing score (Dice Similarity Coefficient (DSC)) is achieved on the final man-made object area mask which separates man-made objects well from natural scene. Future work will exploit the use of multispectral polarimetric imagery for target classification using machine learning techniques.
Images acquired through underwater turbulent media make the image processing tasks in image restoration and object identification challenging. Turbulence in water is associated with random fluctuations of temperature and salinity. These fluctuations are responsible for changing the refractive index, for attenuating illumination, imposing geometric distortions and space-variant blur on images, thus making object identification more difficult. In this paper, we propose a patch-wise deconvolution procedure for removing the space-variant blur from images for restoration purpose prior to resolving the object identification issue. The deconvolution procedure is aided with an image alignment procedure for obtaining better results. Next, an image segmentation algorithm based on fuzzy clustering is considered for object identification. Computational experiments are conducted using a real-world dataset to demonstrate the efficiency of the proposed method.
Recently, we proposed an inexpensive deformable mirror made of Poly-Vinylidene Fluoride-(PVDF), called Vibrating Membrane Mirror (VMM), to compensate for optical atmospheric aberrations [1, 2]. The degree of similarity between the vibration mode shapes of a circular membrane and Zernike polynomials were investigated and VMM was introduced as a promising alternative to the traditional deformable mirrors. The present work deals with technical concepts, design, and surface analysis of the proposed deformable mirror. The mode identification, dynamic range, and time response of the proposed mirror is discussed and important factors that influence these parameters are investigated. To measure the mirror surface motion, a Laser Doppler vibrometer is used. Results show that the mechanical performance of the VMM satisfies the basic requirements of an optical deformable mirror. The mirror performance is optically examined in an interferometer setup and recommendations are provided to improve it.
KEYWORDS: Point spread functions, Single photon emission computed tomography, Modulation transfer functions, Spatial resolution, Monte Carlo methods, Reconstruction algorithms, Collimators, Imaging systems, Fourier transforms
Light field SPECT (L-SPECT) is an improved version of SPECT and works by introducing the concept of plenoptic imaging to reduce scanning time and to increase the amount of detected information. In L-SPECT, a tungsten pinhole array is used as a collimator to differentiate the incoming direction of radiation, rather than only allowing radiation from a set direction dictated by a conventional tube collimator. The distance of the pinhole array to the sensors’ plane is so that the sensors behind each pinhole are only exposed through that pinhole alone. This paper investigates the effects of the pinholes’ diameter and pitch over the reconstruction resolution using simulation experiments. In this proposed reconstruction algorithm, a ray is back projected from the centre of each detector with non-zero pixel value via the corresponding pinhole’s centre, and towards the area of interest with 128×128×128 voxels. The projected rays’ intersections are identified by using ray tracing and the voxels at which they intersect are updated by incrementing with the sum of the pixel values from each detector involved. Experiments are conducted with pinhole arrays of 100×100, 50×50, 30×30 and pinhole diameter of 0.5mm, 1mm and 2mm. Reconstruction is conducted for various simulated objects. Results indicate that when the number of pinholes is increased, the diameter of the pinholes should be reduced to maintain spatial resolution. Moreover, a reconstruction performed by using only 12 projections shows similar quality for the same with 36 and 72 projections. The analysis of the proposed reconstruction algorithm shows that it improves spatial resolution over the filtered back projection algorithm. Reconstruction quality can be further improved by considering scattering loss and photon attenuation.
Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.
Underwater turbulence occurs due to random fluctuations of temperature and salinity in the water. These fluctuations are responsible for variations in water density, refractive index and attenuation. These impose random geometric distortions, spatio-temporal varying blur, limited range visibility and limited contrast on the acquired images. There are some restoration techniques developed to address this problem, such as image registration based, lucky region based and centroid-based image restoration algorithms. Although these methods demonstrate better results in terms of removing turbulence, they require computationally intensive image registration, higher CPU load and memory allocations. Thus, in this paper, a simple patch based dictionary learning algorithm is proposed to restore the image by alleviating the costly image registration step. Dictionary learning is a machine learning technique which builds a dictionary of non-zero atoms derived from the sparse representation of an image or signal. The image is divided into several patches and the sharp patches are detected from them. Next, dictionary learning is performed on these patches to estimate the restored image. Finally, an image deconvolution algorithm is employed on the estimated restored image to remove noise that still exists.
A novel vibrating membrane mirror (VMM) based on the mechanical concepts of vibrating membranes is proposed. This mirror has the capability of being a proper alternative for the traditional optical mirrors. A finite element model of membrane mirror is developed using ANSYS Workbench and its dynamic characteristics extracted to compare with main wavefront aberrations. The similarities between normal modes of a vibrating membrane and Zernike polynomials, which approximate mathematically distorted wavefront, are investigated and the degree of similarities is calculated using RMSE criteria and effective radius. To eliminate unwelcome vibrations of actuators, the excitation ring has been introduced.
This paper aims to investigate the performance of a newly proposed L-SPECT system for small animal brain imaging. The L-SPECT system consists of an array of 100 × 100 micro range diameter pinholes. The proposed detector module has a 48 mm by 48 mm active area and the system is based on a pixelated array of NaI crystals (10×10×10 mm elements) coupled with an array of position sensitive photomultiplier tubes (PSPMTs). The performance of this system was evaluated with pinhole radii of 50 μm, 60 μm and 100 μm. Monte Carlo simulation studies using the Geant4 Application for Tomographic Emission (GATE) software package validate the performance of this novel dual head L-SPECT system where a geometric mouse phantom is used to investigate its performance. All SPECT data were obtained using 120 projection views from 0° to 360° with a 3° step. Slices were reconstructed using conventional filtered back projection (FBP) algorithm. We have evaluated the quality of the images in terms of spatial resolution (FWHM) based on line spread function, the system sensitivity, the point source response function and the image quality. The sensitivity of our newly proposed L- SPECT system was about 4500 cps/μCi at 6 cm along with excellent full width at half-maximum (FWHM) using 50 μm pinhole aperture at several radii of rotation. The analysis results show the combination of excellent spatial resolution and high detection efficiency over an energy range between 20-160 keV. The results demonstrate that SPECT imaging using a pixelated L-SPECT detector module is applicable in a quantitative study of mouse brain imaging.
Underwater imaging poses significant challenges due to random dynamic distortions caused by reflection and refraction of light through the water waves. Moving object detection in a turbulent medium further imposes complexity in the imaging. In this paper, a new approach is proposed for turbulence compensation of a distorted underwater video while keeping the real motions unharmed. First, a geometrically stable frame is created from the distorted video that contains no moving objects. Then, a robust non-rigid image registration technique is used to estimate the motion vector fields of the distorted frames against the stable frame. The difference images of the distorted frames with respect to the stable frame, and the estimated motion vector fields are used to detect the real motion regions and to generate a mask for each frame to extract those regions. This proposed method is compared with an earlier method through both qualitative and quantitative analysis. Simulation experiments show that the proposed method provides better corrections to the effects of underwater turbulence whilst accurately preserving the moving objects.
Radiolabeled tracer distribution imaging of gamma rays using pinhole collimation is considered promising for small animal imaging. The recent availability of various radiolabeled tracers has enhanced the field of diagnostic study and is simultaneously creating demand for high resolution imaging devices. This paper presents analyses to represent the optimized parameters of a high performance pinhole array detector module using two different characteristics phantoms. Monte Carlo simulations using the Geant4 application for tomographic emission (GATE) were executed to assess the performance of a four head SPECT system incorporated with pinhole array collimators. The system is based on a pixelated array of NaI(Tl) crystals coupled to an array of position sensitive photomultiplier tubes (PSPMTs). The detector module was simulated to have 48 mm by 48 mm active area along with different pinhole apertures on a tungsten plate. The performance of this system has been evaluated using a uniform shape cylindrical water phantom along with NEMA NU-4 image quality (IQ) phantom filled with 99mTc labeled radiotracers. SPECT images were reconstructed where activity distribution is expected to be well visualized. This system offers the combination of an excellent intrinsic spatial resolution, good sensitivity and signal-to-noise ratio along with high detection efficiency over an energy range between 20-160 keV. Increasing number of heads in a stationary system configuration offers increased sensitivity at a spatial resolution similar to that obtained with the current SPECT system design with four heads.
This paper focuses on minimizing the time requirement for CT capture through an innovative simultaneous X-ray capture method. The concept was presented in previous publications with synthetically sampled data from a synthetic phantom. This paper puts emphasis on real data reconstruction where a physical 3D phantom consisting of simple geometric shapes was used for the experiment. For a successful reconstruction of the physical phantom, precise calibration of the setup is ensured in this work. Targeting better reconstruction from minimal number of iterations, the sparsity prior CT reconstruction algorithm proposed by Saha et al. [11]was adapted to work in conjunction with the simultaneous X-ray capture modality. Along with critical evaluations of the experimental findings, this paper focuses on optimal parameter settings to achieve a given reconstruction resolution.
KEYWORDS: 3D image processing, Ultrasonography, 3D acquisition, Information operations, Compressed sensing, Stereoscopy, Wavelets, 3D image reconstruction, Data processing, Wavelet transforms
Ultrasound (US) imaging is one of the most popular medical imaging modalities, with 3D US imaging gaining popularity recently due to its considerable advantages over 2D US imaging. However, as it is limited by long acquisition times and the huge amount of data processing it requires, methods for reducing these factors have attracted considerable research interest. Compressed sensing (CS) is one of the best candidates for accelerating the acquisition rate and reducing the data processing time without degrading image quality. However, CS is prone to introduce noise-like artefacts due to random under-sampling. To address this issue, we propose a combined prior-based reconstruction method for 3D US imaging. A Laplacian mixture model (LMM) constraint in the wavelet domain is combined with a total variation (TV) constraint to create a new regularization regularization prior. An experimental evaluation conducted to validate our method using synthetic 3D US images shows that it performs better than other approaches in terms of both qualitative and quantitative measures.
This paper presents an algorithm for recovering an image from a sequence of distorted versions of it, where the distortions are caused by a wavy water surface. A robust non-rigid image registration technique is employed to determine the pixel shift maps of all the frames in the sequence against a reference frame. An iterative image dewarping algorithm is applied to correct the geometric distortions of the sequence. A non-local means filter is used to mitigate noise and improve the signal-to-noise ratio (SNR). The performance of our proposed method is compared against the state-of-the-art method. Results show that our proposed method performs significantly better in removing geometric distortions of the underwater images.
3D ultrasound imaging has advantages as a non-invasive and a faster examination procedure capable of displaying volume information in real time. However, its resolution is affected by speckle noise. Speckle reduction and feature preservation are seemingly opposing goals. In this paper, a nonlinear multi-scale complex wavelet diffusion based algorithm for 3D ultrasound imaging is introduced. Speckle is suppressed and sharp edges are preserved by applying iterative multi-scale diffusion on the complex wavelet coefficients. The proposed method is validated using synthetic, real phantom, and clinical 3D images, and it is found to outperform other methods in both qualitative and quantitative measures.
The purpose of this study is to derive optimized parameters for a detector module employing an off-the-shelf X-ray
camera and a pinhole array collimator applicable for a range of different SPECT systems. Monte Carlo simulations using
the Geant4 application for tomographic emission (GATE) were performed to estimate the performance of the pinhole
array collimators and were compared to that of low energy high resolution (LEHR) parallel-hole collimator in a four head
SPECT system. A detector module was simulated to have 48 mm by 48 mm active area along with 1mm, 1.6mm and 2
mm pinhole aperture sizes at 0.48 mm pitch on a tungsten plate. Perpendicular lead septa were employed to verify
overlapping and non-overlapping projections against a proper acceptance angle without lead septa. A uniform shape
cylindrical water phantom was used to evaluate the performance of the proposed four head SPECT system of the pinhole
array detector module. For each head, 100 pinhole configurations were evaluated based on sensitivity and detection
efficiency for 140 keV ɣ-rays, and compared to LEHR parallel-hole collimator. SPECT images were reconstructed based
on filtered back projection (FBP) algorithm where neither scatter nor attenuation corrections were performed. A better
reconstruction algorithm development for this specific system is in progress. Nevertheless, activity distribution was well
visualized using the backprojection algorithm. In this study, we have evaluated several quantitative and comparative
analyses for a pinhole array imaging system providing high detection efficiency and better system sensitivity over a large
FOV, comparing to the conventional four head SPECT system. The proposed detector module is expected to provide
improved performance in various SPECT imaging.
Existing Computed Tomography (CT) systems require full 360 rotation projections. Using the principles of lightfield
imaging, only 4 projections under ideal conditions can be sufficient when the object is illuminated with multiple-point Xray
sources. The concept was presented in a previous work with synthetically sampled data from a synthetic phantom.
Application to real data requires precise calibration of the physical set up. This current work presents the calibration
procedures along with experimental findings for the reconstruction of a physical 3D phantom consisting of simple
geometric shapes. The crucial part of this process is to determine the effective distances of the X-ray paths, which are not
possible or very difficult by direct measurements. Instead, they are calculated by tracking the positions of fiducial
markers under prescribed source and object movements. Iterative algorithms are used for the reconstruction. Customized
backprojection is used to ensure better initial guess for the iterative algorithms to start with.
Ultrasound imaging is a dominant tool for diagnosis and evaluation in medical imaging systems. However, as its major limitation is that the images it produces suffer from low quality due to the presence of speckle noise, to provide better clinical diagnoses, reducing this noise is essential. The key purpose of a speckle reduction algorithm is to obtain a speckle-free high-quality image whilst preserving important anatomical features, such as sharp edges. As this can be better achieved using multiple ultrasound images rather than a single image, we introduce a complex wavelet-based algorithm for the speckle reduction and sharp edge preservation of two-dimensional (2D) ultrasound images using multiple ultrasound images. The proposed algorithm does not rely on straightforward averaging of multiple images but, rather, in each scale, overlapped wavelet detail coefficients are weighted using dynamic threshold values and then reconstructed by averaging. Validation of the proposed algorithm is carried out using simulated and real images with synthetic speckle noise and phantom data consisting of multiple ultrasound images, with the experimental results demonstrating that speckle noise is significantly reduced whilst sharp edges without discernible distortions are preserved. The proposed approach performs better both qualitatively and quantitatively than previous existing approaches.
n this paper, we present an innovative iterative algorithm for tomographic reconstruction. Algebraic reconstruction technique (ART) which is considered as the core of iterative approach has been enhanced to ensure much finer and faster reconstruction. Backprojection has been customized to make it work even when the projections are not uniformly distributed. Contour information of the object has been combined with customized backprojection to ensure a better initial guess to start ART iterations. Based on experiments with both simulated and real medical images it has been shown that the proposed modality is capable of computing more accurate reconstructions in addition with lower computational cost than traditional ART.
Existing Computed Tomography (CT) systems are vulnerable to internal organ movements. This drawback is
compensated by extra exposures and digital processing. CT being a radiation dose intensive modality, it is imperative to
limit the patient’s exposure to X-ray radiation, if only by removing the necessity to take extra exposures. A multiple
pinhole camera, akin to optical lightfield imaging, to acquire simultaneously multiple X-ray projections is presented.
This new method allows a single snapshot acquisition of all necessary projections for 3D reconstruction. It will also
allow the real-time dynamic 3D X-ray reconstruction of moving organs, as it requires no scanning and no moving parts
in its final implementation. A proof-of-concept apparatus that simulates the intended process was built and parallaxed
images were obtained with minor processing. Synthetic 3D reconstruction tests are also presented.
This paper focuses on tomographic reconstruction from a smaller number of projections than usual. Whereas traditional
CT scanner are based on sequential X-ray sources, the proposed methodology in this work is based on simultaneous x-ray
sources on each projection. Simulations have shown that only four projections are needed to reconstruct a slice,
which are captured simultaneously, offering drastic reduction of image capture time. Algebraic Reconstruction
Technique (ART) has been used for reconstruction. Although ART has many advantages over the established
methods, it remained unpopular due to its high computational cost, and most importantly due to the artefacts caused by
the patient's movement during image capture. The simultaneity of the projections helps to overcome this serious
shortcoming of ART.
When imaging through the atmosphere, the resulting image contains not only the desired scene, but also the adverse
effects of all the turbulent air mass between the camera and the scene. These effects are viewed as a combination of nonuniform
blurring and random shifting of each point in the received short-exposure image. Corrections for both aspects of
this combined distortion have been tackled reasonably successfully by previous efforts. We presented in an earlier paper
a more robust method of restoring the geometry by redefining the place of the prototype frame and by reducing the
adverse effect of averaging in the processing sequence. We present here a variant of this method using a Minimum Sum
of Squared Differences (MSSD) cross-correlation registration algorithm implemented on a Graphics Processing Unit
(GPU). The raw speed-up achieved using GPU code is in the order of x1000. Two orders of magnitude speed-up on the
complete algorithm will allow for better fine tuning of this method and for experimentation with various registration
algorithms.
Image registration is one of the most important tasks in image processing and is frequently one of the most
computationally intensive. In cases where there is a high likelihood of finding the exact template in the search image,
correlation-based methods predominate. Presumably this is because the computational complexity of a correlation
operation can be reduced substantially by transforming the task into the frequency domain. Alternative methods such as
minimum Sum of Squared Differences (minSSD) are not so tractable and are normally disfavored.
This bias is justified when dealing with conventional computer processors since the operations must be conducted in an
essentially sequential manner however we demonstrate it is normally unjustified when the processing is undertaken on
customizable hardware such as FPGAs where tasks can be temporally and/or spatially parallelized. This is because the
gate-based logic of an FPGA is better suited to the tasks of minSSD i.e. signed-addition hardware can be very cheaply
implemented in FPGA fabric, and square operations are easily implemented via a look-up table. In contrast, correlationbased
methods require extensive use of multiplier hardware which cannot be so cheaply implemented in the device.
Even with modern DSP-oriented FPGAs which contain many "hard" multipliers we experience at least an order of
magnitude increase in the number of minSSD hardware modules we can implement compared to cross-correlation
modules. We demonstrate successful use and comparison of techniques within an FPGA for registration and correction
of turbulence degraded images.
In our previous work we have demonstrated that the perceived wander of image intensities as seen through the
"windows" of each pixel due to atmospheric turbulence can be modelled as a simple oscillator pixel-by-pixel and a linear
Kalman filter (KF) can be finetuned to predict to a certain extent short term future deformations. In this paper, we are
expanding the Kalman filter into a Hybrid Extended Kalman filter (HEKF) to fine tune itself by relaxing the oscillator
parameters at each individual pixel. Results show that HEKF performs significantly better than linear KF.
When imaging through the atmosphere, the resulting image contains not only the desired scene, but also the adverse effects of all the turbulent air mass between the camera and the scene. These effects are viewed as a combination of non-uniform blurring and random shifting of each point in the received short-exposure image. Corrections for both aspects of this combined distortion have been tackled reasonably successfully by previous efforts. A potentially more robust method of restoring the geometry is presented, which is also better suited to real-time implementation. The improvements were achieved by replacing the concept of prototype frame with the sequential registration of each frame with its nearest neighbour and the accurate accumulation of shiftmaps from any one frame to another without redundant calculations.
Adhesively bonded composite patches employed for repairing fatigue cracks in metallic airframe structural components often fail under peel stress generated in the structure. The application of piezoelectric stress sensors embedded within the bonded joint for direct measurement of the peel stresses is reported here. Polyvinylidine fluoride (PVDF) film of about 28 micron thickness coated with nickel copper is employed to construct hin sensors embedded between the composite patch and the metallic surface of the crotch joint specimen. PVDF sensor with varying sizes were constructed and calibrated using polycarbonate test specimens subjected to uniaxial tension and compression. The sensors were then embedded between the composite patch and the metallic surface of the crotch specimen to monitor the peel stresses in the adhesive. The measurements are compare with stresses in the adhesive. The measurements are compared with stresses predicted by finite element modeling.
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