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Showing papers in "International Journal of Biomedical Imaging in 2014"


Journal ArticleDOI
TL;DR: This paper presents a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes and demonstrates an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
Abstract: Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

87 citations


Journal ArticleDOI
TL;DR: The proposed accelerometer-based method has high potential to be applied in dual gating in clinical cardiac positron emission tomography (PET) to obtain motion-free images in the future.
Abstract: Both respiratory and cardiac motions reduce the quality and consistency of medical imaging specifically in nuclear medicine imaging. Motion artifacts can be eliminated by gating the image acquisition based on the respiratory phase and cardiac contractions throughout the medical imaging procedure. Electrocardiography (ECG), 3-axis accelerometer, and respiration belt data were processed and analyzed from ten healthy volunteers. Seismocardiography (SCG) is a noninvasive accelerometer-based method that measures accelerations caused by respiration and myocardial movements. This study was conducted to investigate the feasibility of the accelerometer-based method in dual gating technique. The SCG provides accelerometer-derived respiratory (ADR) data and accurate information about quiescent phases within the cardiac cycle. The correct information about the status of ventricles and atria helps us to create an improved estimate for quiescent phases within a cardiac cycle. The correlation of ADR signals with the reference respiration belt was investigated using Pearson correlation. High linear correlation was observed between accelerometer-based measurement and reference measurement methods (ECG and Respiration belt). Above all, due to the simplicity of the proposed method, the technique has high potential to be applied in dual gating in clinical cardiac positron emission tomography (PET) to obtain motion-free images in the future.

68 citations


Journal ArticleDOI
TL;DR: An ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method and shows an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images.
Abstract: We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area The brain is centered on a horizontal line and the white matter of the brain's inner ring is split into eight regions In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere Finally, we make corrections and mark the stroke area with red color In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images

42 citations


Journal ArticleDOI
TL;DR: A template-based method for robust labeling of vertebral levels along the whole spinal cord for T1- and T2-weighted contrasts for spinal cord injury is proposed and freely available as part of the spinal cord toolbox.
Abstract: Context. MRI of the spinal cord provides a variety of biomarkers sensitive to white matter integrity and neuronal function. Current processing methods are based on manual labeling of vertebral levels, which is time consuming and prone to user bias. Although several methods for automatic labeling have been published; they are not robust towards image contrast or towards susceptibility-related artifacts. Methods. Intervertebral disks are detected from the 3D analysis of the intensity profile along the spine. The robustness of the disk detection is improved by using a template of vertebral distance, which was generated from a training dataset. The developed method has been validated using T1- and T2-weighted contrasts in ten healthy subjects and one patient with spinal cord injury. Results. Accuracy of vertebral labeling was 100%. Mean absolute error was 2.1 ± 1.7mm for T2-weighted images and 2.3 ± 1.6 mm for T1-weighted images. The vertebrae of the spinal cord injured patient were correctly labeled, despite the presence of artifacts caused by metallic implants. Discussion. We proposed a template-based method for robust labeling of vertebral levels along the whole spinal cord for T1- and T2-weighted contrasts. The method is freely available as part of the spinal cord toolbox.

41 citations


Journal ArticleDOI
TL;DR: A quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms and guidelines for systematically choosing the proper measure of image quality for medical images are provided.
Abstract: Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure's basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.

40 citations


Journal ArticleDOI
TL;DR: This work proposes a novel approach for initializing an active shape model (ASM) and applies it to 3D lung segmentation in CT scans and shows a statistically significant improvement compared to four other approaches.
Abstract: Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

34 citations


Journal ArticleDOI
TL;DR: Multispectral three-dimensional photoacoustic imaging allowed for selective identification of melanoma metastases in human lymph nodes, compared to histopathology as gold standard.
Abstract: Objective. Sentinel node biopsy in patients with cutaneous melanoma improves staging, provides prognostic information, and leads to an increased survival in node-positive patients. However, frozen section analysis of the sentinel node is not reliable and definitive histopathology evaluation requires days, preventing intraoperative decision-making and immediate therapy. Photoacoustic imaging can evaluate intact lymph nodes, but specificity can be hampered by other absorbers such as hemoglobin. Near infrared multispectral photoacoustic imaging is a new approach that has the potential to selectively detect melanin. The purpose of the present study is to examine the potential of multispectral photoacoustic imaging to identify melanoma metastasis in human lymph nodes. Methods. Three metastatic and nine benign lymph nodes from eight melanoma patients were scanned ex vivo using a Vevo LAZR© multispectral photoacoustic imager and were spectrally analyzed per pixel. The results were compared to histopathology as gold standard. Results. The nodal volume could be scanned within 20 minutes. An unmixing procedure was proposed to identify melanoma metastases with multispectral photoacoustic imaging. Ultrasound overlay enabled anatomical correlation. The penetration depth of the photoacoustic signal was up to 2 cm. Conclusion. Multispectral three-dimensional photoacoustic imaging allowed for selective identification of melanoma metastases in human lymph nodes.

32 citations


Journal ArticleDOI
TL;DR: Temperature sensitivity of the fluorescence intensity of the organic dyes solutions was used for noncontact measurement of the electromagnetic millimeter wave absorption in water to detect temperature change in small biological objects.
Abstract: Temperature sensitivity of the fluorescence intensity of the organic dyes solutions was used for noncontact measurement of the electromagnetic millimeter wave absorption in water. By using two different dyes with opposite temperature effects, local temperature increase in the capillary that is placed inside a rectangular waveguide in which millimeter waves propagate was defined. The application of this noncontact temperature sensing is a simple and novel method to detect temperature change in small biological objects.

26 citations


Journal ArticleDOI
TL;DR: Techniques for quantifying transverse strain in skeletal muscle fascicles during contraction from B-mode ultrasound images showed that the transversal strain increases while the longitudinal fascicle length decreases; however, the extent of these strains was smaller than expected.
Abstract: When skeletal muscle fibres shorten, they must increase in their transverse dimensions in order to maintain a constant volume. In pennate muscle, this transverse expansion results in the fibres rotating to greater pennation angle, with a consequent reduction in their contractile velocity in a process known as gearing. Understanding the nature and extent of this transverse expansion is necessary to understand the mechanisms driving the changes in internal geometry of whole muscles during contraction. Current methodologies allow the fascicle lengths, orientations, and curvatures to be quantified, but not the transverse expansion. The purpose of this study was to develop and validate techniques for quantifying transverse strain in skeletal muscle fascicles during contraction from B-mode ultrasound images. Images were acquired from the medial and lateral gastrocnemii during cyclic contractions, enhanced using multiscale vessel enhancement filtering and the spatial frequencies resolved using 2D discrete Fourier transforms. The frequency information was resolved into the fascicle orientations that were validated against manually digitized values. The transverse fascicle strains were calculated from their wavelengths within the images. These methods showed that the transverse strain increases while the longitudinal fascicle length decreases; however, the extent of these strains was smaller than expected.

24 citations


Journal ArticleDOI
TL;DR: An improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography image and high-resolution Blue-light scanner image, which can guarantee the global convergence and the convergence precision.
Abstract: Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point's translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dentalmodel and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data.

22 citations


Journal ArticleDOI
TL;DR: This paper presents a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information and leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain.
Abstract: Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed tobe constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristol's 31-element array configuration.

Journal ArticleDOI
TL;DR: The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and the multinode multithreaded GPU and CUDA software architecture.
Abstract: We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedralelements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second.

Journal ArticleDOI
TL;DR: This study performs a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency- Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts.
Abstract: The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality.

Journal ArticleDOI
TL;DR: A tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images is developed and evaluated and a high spatial frequency suppression method is used to reduce the gray matter area signal intensity.
Abstract: White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to be Y = 1.04X + 1.74 (R2 = 0.96). The automated algorithm estimates the number, volume, and category of WMH.

Journal ArticleDOI
TL;DR: A new active contour model with higher-order diffusion method is proposed that can converge to the edge of the image even with the intensity inhomogeneities and the fast Split Bregman algorithm is designed for the segmentation implementation.
Abstract: Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

Journal ArticleDOI
TL;DR: This paper presents a methodology to compare these algorithms for image segmentation of the cornea endothelium by the way of geometric dissimilarity criteria, and the best parameter values are proposed to the expert ophthalmologists.
Abstract: The cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light transparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of the cells: a high spatial density will result in a good transparency. Thus, the main criterion required by ophthalmologists is the cell density of the cornea endothelium, mainly obtained by an image segmentation process. Different methods can perform the image segmentation of these cells, and the three most performing methods are studied here. The question for the ophthalmologists is how to choose the best algorithm and to obtain the best possible results with it. This paper presents a methodology to compare these algorithms together. Moreover, by the way of geometric dissimilarity criteria, the algorithms are tuned up, and the best parameter values are thus proposed to the expert ophthalmologists.

Journal Article
TL;DR: This paper presents a new approach to perform ICV extraction based on the use of a library of pre- labeled brain images to capture the large variability of brain shapes and demonstrates an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
Abstract: Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and follow-up of many neurological diseases. To estimate such regional brain volumes, the Intracranial Cavity Volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal inter-subject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of pre- labeled brain images to capture the large variability of brain shapes. To this end, an improved non-local label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

Journal ArticleDOI
TL;DR: A novel method is suggested, which applies multiobjective greedy rules and makes fusion of color and space information in order to extract tongue image accurately.
Abstract: Tongue image with coating is of important clinical diagnostic meaning, but traditional tongue image extraction method is not competent for extraction of tongue image with thick coating. In this paper, a novel method is suggested, which applies multiobjective greedy rules and makes fusion of color and space information in order to extract tongue image accurately. A comparative study of several contemporary tongue image extraction methods is also made from the aspects of accuracy and efficiency. As the experimental results show, geodesic active contour is quite slow and not accurate, the other 3 methods achieve fairly good segmentation results except in the case of the tongue with thick coating, our method achieves ideal segmentation results whatever types of tongue images are, and efficiency of our method is acceptable for the application of quantitative check of tongue image.

Journal ArticleDOI
TL;DR: A novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to the LIC algorithm, and a color-coding model for the fused visualization of slices from T1 datasets together with directionally encoded LIC maps is proposed.
Abstract: Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion tensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy mapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an advancement of a previously published multikernel LIC approach for high angular resolution diffusion imaging visualization is proposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to the LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which provide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two-and three-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and branching fibers. Furthermore, a color-coding model for the fused visualization of slices from T1 datasets together with directionally encoded LIC maps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing and bending fibers. In addition, results from in vivo studies with a healthy volunteer and a brain tumor patient are presented to demonstrate the method's practicality.

Journal ArticleDOI
TL;DR: A modified Perona-Malik diffusion (PMD) filter is proposed to enhance a coronary plaque boundary by considering the conditions peculiar to an intravascular ultrasound (IVUS) image and it can not only reduce the speckle noise but also enhance clearly the coronary plaque boundaries.
Abstract: We propose a modified Perona-Malik diffusion (PMD) filter to enhance a coronary plaque boundary by considering the conditions peculiar to an intravascular ultrasound (IVUS) image. The IVUS image is commonly used for a diagnosis of acute coronary syndrome (ACS). The IVUS image is however very grainy due to heavy speckle noise. When the normal PMD filter is applied for speckle noise reduction in the IVUS image, the coronary plaque boundary becomes vague. For this problem, we propose a modified PMD filter which is designed in special reference to the coronary plaque boundary detection. It can then not only reduce the speckle noise but also enhance clearly the coronary plaque boundary. After applying the modified PMD filter to the IVUS image, the coronary plaque boundaries are successfully detected further by applying the Takagi-Sugeno fuzzy model. The accuracy of the proposed method has been confirmed numerically by the experiments.

Journal ArticleDOI
TL;DR: The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.
Abstract: Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents a method for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.

Journal ArticleDOI
TL;DR: A novel 3D medical volume fusion method based on 3D band limited shearlet transform (3D BLST) is proposed and this method is evaluated upon MRI T2* and quantitative susceptibility mapping data of 4 human brains, indicating that the proposed method has a better performance than conventional 2D wavelet, DT CWT, and 3DWavelet,DT CWT based fusion methods.
Abstract: Nowadays many MRI scans can give 3D volume data with different contrasts, but the observers may want to view various contrasts in the same 3D volume. The conventional 2D medical fusion methods can only fuse the 3D volume data layer by layer, which may lead to the loss of interframe correlative information. In this paper, a novel 3D medical volume fusion method based on 3D band limited shearlet transform (3D BLST) is proposed. And this method is evaluated upon MRI T2* and quantitative susceptibility mapping data of 4 human brains. Both the perspective impression and the quality indices indicate that the proposed method has a better performance than conventional 2D wavelet, DT CWT, and 3D wavelet, DT CWT based fusion methods.

Journal ArticleDOI
TL;DR: A semiautomatic segmentation for analysis of WCE images is proposed, based on active contour segmentation, which introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies.
Abstract: Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the "gold standard" technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this paper, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi)polyp(s) segmentation, to radiation enteritis delineation.

Journal ArticleDOI
TL;DR: Experimental results on MR image simulations and real MR data consistently demonstrate that the proposed method can efficiently reconstruct MR images from highly undersampled k-space data and presents advantages over the current state-of-the-art reconstruction approaches, in terms of higher PSNR and lower HFEN values.
Abstract: Nonconvex optimization has shown that it needs substantially fewer measurements than l1 minimization for exact recovery under fixed transform/overcomplete dictionary. In this work, two efficient numerical algorithms which are unified by the method named weighted two-level Bregman method with dictionary updating (WTBMDU) are proposed for solving lp optimization under the dictionary learning model and subjecting the fidelity to the partial measurements. By incorporating the iteratively reweighted norm into the two-level Bregman iteration method with dictionary updating scheme (TBMDU), the modified alternating direction method (ADM) solves the model of pursuing the approximated lp-norm penalty efficiently. Specifically, the algorithms converge after a relatively small number of iterations, under the formulation of iteratively reweighted l1 and l2 minimization. Experimental results on MR image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently demonstrate that the proposed method can efficiently reconstruct MR images from highly undersampled k-space data and presents advantages over the current state-of-the-art reconstruction approaches, in terms of higher PSNR and lower HFEN values.

Journal ArticleDOI
TL;DR: Results show that the proposed model not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios.
Abstract: Validation and accuracy assessment are themain bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed. The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: intrasubject rigid and affine registration of magnetic resonance images. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposed model not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios.

Journal ArticleDOI
TL;DR: The NREM is a nonreference reconstruction based edge map evaluation (NREM) based on comparing the similarity between the reconstructed image with the original image using this concept and experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.
Abstract: Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

Journal ArticleDOI
TL;DR: The proposed method is noninvasive and able to simultaneously capture the spatial and the temporal propagation of wavy patterns along the walls of the carotid artery in a completely automated manner and confirms the potential of the proposed method in providing useful information which can help in revealing the physiological condition of the cardiovascular system.
Abstract: A novel method for characterizing and visualizing the progression of waves along the walls of the carotid artery is presented. The new approach is noninvasive and able to simultaneously capture the spatial and the temporal propagation of wavy patterns along the walls of the carotid artery in a completely automated manner. Spatiotemporal and spatiospectral 2D maps describing these patterns (in both the spatial and the frequency domains, resp.) were generated and analyzed by visual inspection as well as automatic feature extraction and classification. Three categories of cases were considered: pathological elderly, healthy elderly, and healthy young cases. Automatic differentiation, between cases of these three categories, was achieved with a sensitivity of 97.1% and a specificity of 74.5%. Two features were proposed and computed to measure the homogeneity of the spatiospectral 2D map which presents the spectral characteristics of the carotid artery wall's wavy motion pattern which are related to the physical, mechanical (e.g., elasticity), and physiological properties and conditions along the artery. These results are promising and confirm the potential of the proposed method in providing useful information which can help in revealing the physiological condition of the cardiovascular system.