SignificancefNIRS-based neuroenhancement depends on the feasible detection of hemodynamic responses in target brain regions. Using the lateral occipital complex (LOC) and the fusiform face area (FFA) in the ventral visual pathway as neurofeedback targets boosts performance in visual recognition. However, the feasibility of utilizing fNIRS to detect LOC and FFA activity in adults remains to be validated as the depth of these regions may exceed the detection limit of fNIRS.AimThis study aims to investigate the feasibility of using fNIRS to measure hemodynamic responses in the ventral visual pathway, specifically in the LOC and FFA, in adults.ApproachWe recorded the hemodynamic activities of the LOC and FFA regions in 35 subjects using a portable eight-channel fNIRS instrument. A standard one-back object and face recognition task was employed to elicit selective brain responses in the LOC and FFA regions. The placement of fNIRS optodes for LOC and FFA detection was guided by our group’s transcranial brain atlas (TBA).ResultsOur findings revealed selective activation of the LOC target channel (CH2) in response to objects, whereas the FFA target channel (CH7) did not exhibit selective activation in response to faces.ConclusionsOur findings indicate that, although fNIRS detection has limitations in capturing FFA activity, the LOC region emerges as a viable target for fNIRS-based detection. Furthermore, our results advocate for the adoption of the TBA-based method for setting the LOC target channel, offering a promising solution for optrode placement. This feasibility study stands as the inaugural validation of fNIRS for detecting cortical activity in the ventral visual pathway, underscoring its ecological validity. We suggest that our findings establish a pivotal technical groundwork for prospective real-life applications of fNIRS-based research.
Avoiding adverse effects of staining reagents on cellular viability and cell signaling, label-free cell imaging and analysis is essential to personalized genomics, drug development, and cancer diagnostics. By analyzing the images of cells, imagebased cell analytic methodologies offer a relatively simple and economical way to understand the cell heterogeneities and developments. Owing to the developments in high-resolution image sensors and high-performance computation processors, the emerging lens-less digital holography techniques enable a simple and cost-effective approach to obtain label-free cell images with large field of view and microscopic spatial resolution. In this work, the lens-less digital holography technique is adopted for image-based cell analysis. The holograms of three kinds of cells which are MDA-MB231, EC-109 and MCF-10A respectively were recorded by a lens-less digital holography system composed of a laser diode, a sample holder, a sensor and a laptop computer. The acquired holograms are first high-pass filtered. Then the amplitude images were reconstructed using the angular spectrum method and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. The convolutional neural network (CNN) was used to classify the cells. The experiments show that an accuracy of 97.2% can be achieve for two type cell classification and 91.2% for three type cell classification. It is believed that the lens-less holography combining with machine learning holds great promise in the application of stainless cell imaging and classification.
The quad-head PET system has a compact structure which leads to the depth of interaction (DOI) blurring. The Monte Carlo (MC) simulation can eliminate the DOI effect significantly, and it has been utilized in the dual-head PET systems. For the quad-head PET system, the geometric symmetry is less, which makes the MC simulation difficult. The multi-ray method combined with the DOI model can also relieve the effect of the DOI blurring, but it is time-consumed. In this study, we focus on the rapid construction of the system response matrix (SRM) based on geometric symmetries for the multi-ray method. During the construction of the SRM, the SRM is divided into two parts: the SRM of the opposite detectors and the SRM of the adjacent detectors. The general processor unit (GPU) is utilized to improve the computation speed. The result shows that the computation time is largely decreased when the geometric symmetries are used. The simulation experiments indicate that the data of adjacent detector heads and the DOI model are helpful to improve the quality of the quad-head PET reconstruction.
Accurate segmentation of bladder cancer is the basis for determining the staging of bladder cancer. In our previous study, we have segmented the inner and outer surface of bladder wall and obtained the candidate region of bladder cancer, however, it is hard to segment the cancer region from the candidate region. To segment the cancer region accurately, we proposed a voxel-feature-based method and extracted 1159 features from each voxel of candidate region. After feature extraction, the recursive feature elimination-based support vector machine classifier (SVM-RFE) method was adopted to obtain an optimal feature subset for the classification of the cancer and the wall regions. According to feature selection and ranking, 125 top-ranked features were selected as the optimal subset, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 1, 99.99%, 99.98%, and 1. Using the optimal subset, we calculated the probability value of each voxel belonging to the cancer region, then obtained the boundary to separate the tumor and wall regions. The mean DSC of the segmentation results in the testing set is 0.9127, indicating that the proposed method can accurately segment the bladder cancer region.
The existing hybrid cardiac imaging approaches focus on predicting the adverse cardiac events or disease diagnosis, yet do not offer any insight into the pathological advance in the repair process. Angiogenesis is one of the most important mechanism in the repair process after ischemic injury and has shown benefit to the prognosis of occlusive cardiovascular disorders, thus becomes a target of molecular therapies. In vivo monitoring of angiogenesis and comprehensive evaluation of cardiac function associated with angiogenesis are urgently needed in both research and clinical practice. In this paper, a multimodality image fusion strategy was proposed for angiogenesis and viable myocardium identification. Imaging approaches including coronary computed tomography angiography(CCTA), 2-deoxy-2-[18F]fluoro-D-glucose ([18F]DG) PET/CT, [68Ga]-1,4,7-triazacyclononane-1,4,7-triacetic acid-(Arg-Gly-Asp)2 ([68Ga]-NOTA-PRGD2) PET/CT and 99mTc-sestamibi (99mTc-MIBI) myocardial perfusion SPECT/CT scanning were performed to acquire both anatomy and three kinds of function information. All of these modality images were then fused by an automatic strategy consisting of ROI segmentation and cross modality registration. The left ventricle myocardium was categorized into 4 groups based on fusion result according to the respective relative tracer uptake. The final results intuitively reflected the extent of the [18F]DG and 99mTc-MIBI uptake defect, the perfusion-metabolism mismatch area, as well as the location of the [68Ga]-NOTA-PRGD2 signal. The hybrid CCTA-PET-SPECT image verified the occurrence of angiogenesis based on the in vivo noninvasive molecular imaging approaches and visualized the hibernating myocardium. The presented fusion strategy is helpful in facilitating the study of the relationship between viability, perfusion and blocked coronary arteries, as well as angiogenesis.
Raman spectroscopic imaging can provide three-dimensional data set of samples, including two-dimensional spatial image and one-dimensional Raman spectral data. Currently, three strategies can be used to achieve Raman spectroscopic imaging, including point scanning, line scanning, and wide-field illumination. Point scanning method provides the best resolution but has low imaging speed. On the contrary, wide-field illumination can image fast but provides lower spatial resolution. To integrate the advantages of two methods, a new strategy for large-field Raman spectroscopic imaging was proposed, which uses the frequency modulation based spatially encoded light as the excitation. In this method, millions of single beams simultaneously illuminate on the sample to act as the wide-field illumination. Each beam illuminates on different positions of the sample, whose intensity are modulated with different frequencies. Thus, each excitation beam has its own modulation frequency and the excited Raman signal will carry the modulation information. At the detection end, a single point detector was used to collect the time series Raman signals carrying the unique modulation information. Using the sparse reconstruction based on demodulation strategy, the Raman image can be recovered effectively. The feasibility of the method was verified with numerical simulations. The results showed that it is feasible to conduct Raman spectroscopic imaging with high-resolution and high speed under the illumination of frequency modulation based spatially encoded light and the detection of single-point detector.
Raman tomography can provide quantitative distribution of chemicals in a three-dimensional volume with a non-invasive and label-free manner. In view of the problems of existing data collection strategy, a frequency modulation and spatial encoding based Raman tomography was proposed, which aims to improve the data collection scheme and reduce the data collection time. In this scheme, the laser beam was divided into several sub-beams to use as multipoint excitation light sources. These sub-beams were first modulated with different frequencies and then incident on the different points of sample surface simultaneously. Because the excited Raman signals would carry such modulation information, the Raman signals from which excitation position can be distinguished with the demodulation process. In detection end, the Raman scattering light first passed through a spatial-encoding mask and then was directed to the single photomultiplier tube. By changing the pattern of the mask and then performing recovery with sparse reconstruction, the distribution of the Raman signals on the sample surface can be obtained based on compressive sensing theory. Preliminary results showed that our scheme can recover the Raman images to the certain extent with a better signal-to-noise ratio, demonstrating the proposed scheme is feasible.
Stimulated Raman scattering (SRS) microscopy has been increasingly used in biology and medicine in recent years due to its ability to image chemical bonds without labelling. Traditional SRS imaging uses Gaussian beams as the excitation sources, which can achieve high spatial and axial resolutions because of the tight focus of the Gaussian beam. However, the tight focus poses serious problems for observing the scattering media. The Gaussian beam would defocus after propagating through a small distance in scattering media. The SRS microscopy cannot work well in this case. Having the self-healing property, Bessel beams may bring solution to this problem. In our previous work, we applied the Bessel beams to the SRS and implemented three-dimensional SRS imaging with projection concept. Here, we simulated the propagation of Bessel beams and the generation of SRS signals with the beam propagation method (BPM). By adding glass beads on the beam propagation path to simulate scatters, the propagation of the Bessel beams and the generation of the SRS signals would change. We designed a series of simulations to investigate the influence of the size and position of the added glass beads to the generation of SRS signals. Simulation results demonstrated that the SRS signals can generate or be recovered at the certain depth in scattering media.
Cerenkov fluorescence imaging (CLI) has set a bridge between optical and nuclear imaging technologies by using an optical method to detect the distribution of radiotracers. Combining the emerged CLI technique with a clinical endoscope, the Cerenkov luminescence endoscope (CLE) was developed to avoid the problem of the poor penetration depth of the Cerenkov light. However, due to low energy of the Cerenkov light and the transportation loss during endoscopic imaging, the acquisition time of CLE signal is long and the imaging results are poor, which has limited the clinical applications of CLE. There are two ways to improve the availability of the current CLE system. First is to enhance the emitted signals of the Cerenkov light at the source end by developing new kinds of imaging probes or selecting high yield radionuclides. However, this will introduce the in vivo unfriendly problem in clinical translations. The second method is to improve the detection sensitivity of CLE system by optimizing the structure of the system. Here, we customized four endoscopes with different field of view (FOV) angles of endoscope probe and different monofilament diameters of imaging fiber bundles. By comparing the results obtained by different CLE systems, we optimized the parameters of system. The CLE imaging of 18F-FDG showed that when the distance between the probe and radionuclide source was fixed, smaller angle of FOV and lager monofilament diameter will provide higher collection efficiency.
Optical projection tomography(OPT) provides an approach to recreating three-dimensional images of small biological specimens. Light traverses through a straight line to achieve a homogeneous illumination of the specimen. As the specimens in the conventional OPT could not survive or the survival time was too short, this paper proposes a new type of sample fixation method for OPT imaging. The specimen was anaesthetized in a petri dish, and the dish was fixed under the rotational stage of our homemade OPT system for imaging. This method can reduce the damage to the specimen and be more conducive to the continuous observation for in vivo OPT. However, the sample fixation causes the problem of insufficient sampling. To obtain optical projection tomographic image with insufficient samples, this paper uses the iterative reconstruction algorithm combining with the prior information to solve the inverse reconstruction problem.
KEYWORDS: Reconstruction algorithms, Image quality, Tomography, Raman spectroscopy, 3D image processing, Head, 3D acquisition, Data modeling, Data acquisition, Sensors
As an emerging volumetric imaging technique, Stimulated Raman projection tomography (SRPT) can provide quantitative distribution of chemical components in a three-dimensional (3D) volume, with a label-free manner. Currently, the filtered back-projection (FBP) algorithm is used to reconstruct the 3D volume in SRPT. However, to obtain a satisfactory reconstruction result, the FBP algorithm requires a certain amount of projection data, usually, at least 180 projections in a half circle. This leads to a long data acquisition time and hence limits dynamic and longitudinal observation of living systems. Iterative reconstruction from sparsely sampled data may reduce the total data acquisition time by reducing the projections used in the reconstruction. In this work, two total variation regularization based iterative reconstruction algorithms were selected and used in SRPT, including the simultaneous algebra reconstruction technique (SART) and the two-step iterative shrinkage/thresholding algorithm (TwIST). The well-known distance-driven model was utilized as the forward and back-projectors. We evaluated these two algorithms with numerical simulations. Using the original image as the reference, we calculated the quality of the reconstructed images. Simulation results showed that both the SART and TwIST performed better than the FBP algorithm, with larger values of the structural similarity (SSIM). Furthermore, the number of projection images can be largely reduced when the iterative reconstruction algorithm was used. Especially when the SART was used, the projection number can be reduced to 15, providing a satisfactory reconstruction image (SSIM is larger than 0.9).
Coronary artery disease (CAD) is one of the leading causes of death worldwide. The computed tomography angiography (CTA) is increasingly used to diagnose CAD due to its non-invasive nature and high-resolution three-dimensional (3D) imaging capability of the coronary artery anatomy. CTA allows for identification and grading of stenosis by evaluating the degree of narrowing of the blood-filled coronary artery lumen. Both identification and grading rely on the precise segmentation of the coronary arteries on CTA images. In this paper, a fully automatic segmentation framework is proposed to extract the coronary arteries from the whole cardiac CTA images. The framework adopts a paired multi-scale 3D deep convolutional neural networks (CNNs) to identify which voxels belong to the vessel lumen. Voxels that may belong to coronary artery lumen are recognized by the first CNN in the pair and both artery positives and artery-like negatives are distinguished by the second one. Each CNN is assigned to a different task. They share the same architecture in common but with different weights. In order to combine local and larger contextual information, we adopt a dual pathway architecture that can process the input image simultaneously on multiple scales. The experiments were performed on a CTA dataset from 44 patients. 35 CTA scans are used for training and the rests for testing. The proposed segmentation framework achieved a mean Dice similarity coefficient (DSC) of 0.8649 and mean surface distance (MSD) of 0.5571 with reference to manual annotations. Experimental results show that the proposed framework is capable of performing complete, accurate and robust segmentation of the coronary arteries.
For early detection and targeted therapy, receptor expression profiling is instrumental to classifying breast cancer into
sub-groups. In particular, human epidermal growth factor receptor 2 (HER2) expression has been shown to have both
prognostic and predictive values. Recently, an increasingly more complex view of HER2 in breast cancer has emerged
from genome sequencing that highlights the role of inter- and intra-tumor heterogeneity in therapy resistance. Studies on
such heterogeneity demand high-content, high-resolution functional and molecular imaging in vivo, which cannot be
achieved using any single imaging tool. Clearly, there is a critical need to develop a multimodality approach for breast
cancer imaging. Since 2006, grating-based x-ray imaging has been developed for much-improved x-ray images. In 2014,
the demonstration of fluorescence molecular tomography (FMT) guided by x-ray grating-based micro-CT was reported
with encouraging results and major drawbacks. In this paper, we propose to integrate grating-based x-ray tomography
(GXT) and high-dimensional optical tomography (HOT) into the first-of-its-kind truly-fused GXT-HOT (pronounced as
“Get Hot”) system for imaging of breast tumor heterogeneity, HER2 expression and dimerization, and therapeutic
response. The primary innovation lies in developing a brand-new high-content, high-throughput x-ray optical imager
based on several contemporary techniques to have MRI-type soft tissue contrast, PET-like sensitivity and specificity, and
micro-CT-equivalent resolution. This system consists of two orthogonal x-ray Talbot-Lau interferometric imaging chains
and a hyperspectral time-resolved single-pixel optical imager. Both the system design and pilot results will be reported in
this paper, along with relevant issues under further investigation.
The aim of this article is to investigate the influence of a tracer injection dose (ID) and camera integration time (IT) on quantifying pharmacokinetics of Cy5.5-GX1 in gastric cancer BGC-823 cell xenografted mice. Based on three factors, including whether or not to inject free GX1, the ID of Cy5.5-GX1, and the camera IT, 32 mice were randomly divided into eight groups and received 60-min dynamic fluorescence imaging. Gurfinkel exponential model (GEXPM) and Lammertsma simplified reference tissue model (SRTM) combined with a singular value decomposition analysis were used to quantitatively analyze the acquired dynamic fluorescent images. The binding potential (Bp) and the sum of the pharmacokinetic rate constants (SKRC) of Cy5.5-GX1 were determined by the SRTM and EXPM, respectively. In the tumor region, the SKRC value exhibited an obvious trend with change in the tracer ID, but the Bp value was not sensitive to it. Both the Bp and SKRC values were independent of the camera IT. In addition, the ratio of the tumor-to-muscle region was correlated with the camera IT but was independent of the tracer ID. Dynamic fluorescence imaging in conjunction with a kinetic analysis may provide more quantitative information than static fluorescence imaging, especially for a priori information on the optimal ID of targeted probes for individual therapy.
For fluorescence tomographic imaging of small animals, the liver is usually regarded as a low-scattering tissue and is surrounded by adipose, kidneys, and heart, all of which have a high scattering property. This leads to a breakdown of the diffusion equation (DE)–based reconstruction method as well as a heavy computational burden for the simplified spherical harmonics equation (SPN). Coupling the SPN and DE provides a perfect balance between the imaging accuracy and computational burden. The coupled third-order SPN and DE (CSDE)-based reconstruction method is developed for fluorescence tomographic imaging. This is achieved by doubly using the CSDE for the excitation and emission processes of the fluorescence propagation. At the same time, the finite-element method and hybrid multilevel regularization strategy are incorporated in inverse reconstruction. The CSDE-based reconstruction method is first demonstrated with a digital mouse-based liver cancer simulation, which reveals superior performance compared with the SPN and DE-based methods. It is more accurate than the DE-based method and has lesser computational burden than the SPN-based method. The feasibility of the proposed approach in applications of in vivo studies is also illustrated with a liver cancer mouse-based in situ experiment, revealing its potential application in whole-body imaging of small animals.
Fluorescence molecular tomography (FMT) is an important imaging technique of optical imaging. The major challenge of the reconstruction method for FMT is the ill-posed and underdetermined nature of the inverse problem. In past years, various regularization methods have been employed for fluorescence target reconstruction. A comparative study between the reconstruction algorithms based on l 1 -norm and l 2 -norm for two imaging models of FMT is presented. The first imaging model is adopted by most researchers, where the fluorescent target is of small size to mimic small tissue with fluorescent substance, as demonstrated by the early detection of a tumor. The second model is the reconstruction of distribution of the fluorescent substance in organs, which is essential to drug pharmacokinetics. Apart from numerical experiments, in vivo experiments were conducted on a dual-modality FMT/micro-computed tomography imaging system. The experimental results indicated that l 1 -norm regularization is more suitable for reconstructing the small fluorescent target, while l 2 -norm regularization performs better for the reconstruction of the distribution of fluorescent substance.
Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.
As one of molecular imaging, bioluminescence tomography (BLT) aims to recover internal source from surface
measurement. Being an ill-posed inverse problem, BLT source reconstruction is usually converted to an optimization
problem through regularization. In this contribution, we build a bimodal hybrid imaging system consisting of BLT and
micro-CT, and then propose an improved source reconstruction method based on adjoint diffusion equations (ADEs).
Compared with conventional methods based on constrained minimization problem (CMP), ADEs-based method replaces
expensive iterative computation with solving a group of linear ADEs. Given surface flux density, internal source power
density and photon fluence rate can be efficiently determined in one step. Both numerical and physical experiments are
performed to evaluate the bimodal BLT/micro-CT imaging system and this novel reconstruction method. The relevant
results demonstrate the feasibility and potential of this source reconstruction method.
Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has
been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric
cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer
metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The
bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues.
Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not
optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction
algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The
radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe
light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is
converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of
bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital
mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based
experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early
gastric cancer detection.
The optical imaging takes advantage of coherent optics and has promoted the development of visualization of biological
application. Based on the temporal coherence, optical coherence tomography can deliver three-dimensional optical
images with superior resolutions, but the axial and lateral scanning is a time-consuming process. Optical scanning
holography (OSH) is a spatial coherence technique which integrates three-dimensional object into a two-dimensional
hologram through a two-dimensional optical scanning raster. The advantages of high lateral resolution and fast image
acquisition offer it a great potential application in three-dimensional optical imaging, but the prerequisite is the accurate
and practical reconstruction algorithm. Conventional method was first adopted to reconstruct sectional images and
obtained fine results, but some drawbacks restricted its practicality. An optimization method based on 2 l norm obtained
more accurate results than that of the conventional methods, but the intrinsic smooth of 2 l norm blurs the reconstruction
results. In this paper, a hard-threshold based sparse inverse imaging algorithm is proposed to improve the sectional image
reconstruction. The proposed method is characterized by hard-threshold based iterating with shrinkage threshold strategy,
which only involves lightweight vector operations and matrix-vector multiplication. The performance of the proposed
method has been validated by real experiment, which demonstrated great improvement on reconstruction accuracy at
appropriate computational cost.
Because of the ability of integrating the strengths of different modalities and providing fully integrated information,
multi-modality molecular imaging techniques provide an excellent solution to detecting and diagnosing earlier cancer,
which remains difficult to achieve by using the existing techniques. In this paper, we present an overview of our research
efforts on the development of the optical imaging-centric multi-modality molecular imaging platform, including the
development of the imaging system, reconstruction algorithms and preclinical biomedical applications. Primary
biomedical results show that the developed optical imaging-centric multi-modality molecular imaging platform may
provide great potential in the preclinical biomedical applications and future clinical translation.
Immunocytochemical and immunofluorescence staining are used for identifying the characteristics of metastasis in
traditional ways. Micro-computed tomography (micro-CT) is a useful tool for monitoring and longitudinal imaging of
tumor in small animal in vivo. In present study, we evaluated the feasibility of the detection for metastasis of gastric
carcinoma by high-resolution micro-CT system with omnipaque accumulative enhancement method in the organs.
Firstly, a high-resolution micro-CT ZKKS-MCT-sharp micro-CT was developed by our research group and Guangzhou
Zhongke Kaisheng Medical Technology Co., Ltd. Secondly, several gastric carcinoma models were established through
inoculating 2x106 BGC-823 gastric carcinoma cells subcutaneously. Thirdly, micro-CT scanning was performed after
accumulative enhancement method of intraperitoneal injection of omnipaque contrast agent containing 360 mg iodine
with a concentration of 350 mg I/ml. Finally, we obtained high-resolution anatomical information of the metastasis in
vivo in a BALB/c NuNu nude mouse, the 3D tumor architecture is revealed in exquisite detail at about 35 μm spatial
resolution. In addition, the accurate shape and volume of the micrometastasis as small as 0.78 mm3 can be calculated
with our software. Overall, our data suggest that this imaging approach and system could be used to enhance the
understanding of tumor proliferation, metastasis and could be the basis for evaluating anti-tumor therapies.
Although top-down perceptual process plays an important role in face processing, its neural substrate is still puzzling
because the top-down stream is extracted difficultly from the activation pattern associated with contamination caused by
bottom-up face perception input. In the present study, a novel paradigm of instructing participants to detect faces from
pure noise images is employed, which could efficiently eliminate the interference of bottom-up face perception in topdown
face processing. Analyzing the map of functional connectivity with right FFA analyzed by conventional Pearson's
correlation, a possible face processing pattern induced by top-down perception can be obtained. Apart from the brain
areas of bilateral fusiform gyrus (FG), left inferior occipital gyrus (IOG) and left superior temporal sulcus (STS), which
are consistent with a core system in the distributed cortical network for face perception, activation induced by top-down
face processing is also found in these regions that include the anterior cingulate gyrus (ACC), right oribitofrontal cortex
(OFC), left precuneus, right parahippocampal cortex, left dorsolateral prefrontal cortex (DLPFC), right frontal pole,
bilateral premotor cortex, left inferior parietal cortex and bilateral thalamus. The results indicate that making-decision,
attention, episodic memory retrieving and contextual associative processing network cooperate with general face
processing regions to process face information under top-down perception.
Recently, there were debates about the neural substrate of face processing, namely, whether the lateral middle fusiform
was involved in face processing of visual expertise and categorization at individual level or specialized only for face
processing. In the present study, Chinese characters were taken as the ideal comparison stimuli to reveal the neural
substrate for face processing, due to their high similarity to faces on a variety of dimensions. The results demonstrated
there was very strong correlation between the activation pattern elicited by faces and characters in the fusiform, whereas
the greater response was observed for faces than for characters in the right middle lateral fusiform gyrus, suggesting that
FFA may be a special neural substrate for face processing.
Image segmentation is a fundamental image processing technology. There are many kinds of image segmentation methods, but most of them are problem oriented. In this paper, image segmentation method based on lateral inhibition network is presented. Lateral inhibition network is a biological vision model. When an image is filtered by a lateral inhibition network, its low frequency components are inhibited while the high frequency components are enhanced. The lateral inhibited image is much easier to be segmented because of its increased inter-class difference and decreased intra-class difference. The parameters of the lateral inhibition network model determine the inhibited image, thus affect the image segmentation result greatly. But there are no assured rules to determine the parameters. We propose an evolutionary strategy (ES) based method to search the optimal weighting parameters of the lateral inhibition network model. The objective function of ES is a multiattribute fitness function that combines multiple criteria of clustering and entropy information. The original image is filtered using the optimal lateral inhibition network and then the inhibited image is segmented by an optimized threshold. Using test images of various characteristics, the proposed method is evaluated by four objective image segmentation evaluation indexes. The experimental results show its validity and universality.
Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further automation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and values of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new approach. On-site quantitative and qualitative evaluations of the various decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms traditional enhancement techniques with a noted increase of 58% in low-density threat detection rates.
A multiple neural network classifier fusion system design method using immune genetic algorithm (IGA) is proposed. The IGA is modeled after the mechanics of human immunity. By using vaccination and immune selection in the evolution procedures, the IGA outperforms the traditional genetic algorithms in restraining the degenerate phenomenon and increasing the converging speed. The fusion system consists of N neural network classifiers that work independently and in parallel to classify a given input pattern. The classifiers' outputs are aggregated by a fusion scheme to decide the collective classification results. The goal of the system design is to obtain a fusion system with both good generalization and efficiency in space and time. Two kinds of measures, the accuracy of classification and the size of the neural networks, are used by IGA to evaluate the fusion system. The vaccines are abstracted by a self-adaptive scheme during the evolutionary process. A numerical experiment on the 'alternate labels' problem is implemented and the comparisons of IGA with traditional genetic algorithm are presented.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.