Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.

BACKGROUND: Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames. METHODS: In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy. RESULTS: 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study. CONCLUSION: The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.

Altered brain structural and functional connectivity in cannabis users.

Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22-36, 19 female) in comparison with 73 healthy controls (age 22-36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.

Temporally dynamic neural correlates of drug cue reactivity, response inhibition, and methamphetamine-related response inhibition in people with methamphetamine use disorder.

Cue-induced drug craving and disinhibition are two essential components of continued drug use and relapse in substance use disorders. While these phenomena develop and interact across time, the temporal dynamics of their underlying neural activity remain under-investigated. To explore these dynamics, an analysis of time-varying activation was applied to fMRI data from 62 men with methamphetamine use disorder in their first weeks of recovery in an abstinence-based treatment program. Using a mixed block-event, factorial cue-reactivity/Go-NoGo task and a sliding window across the task duration, dynamically-activated regions were identified in three linear mixed effects models (LMEs). Habituation to drug cues across time was observed in the superior temporal gyri, amygdalae, left hippocampus, and right precuneus, while response inhibition was associated with the sensitization of temporally-dynamic activations across many regions of the inhibitory frontoparietal network. Methamphetamine-related response inhibition was associated with temporally-dynamic activity in the parahippocampal gyri and right precuneus (corrected p-value < 0.001), which show a declining cue-reactivity contrast and an increasing response inhibition contrast. Overall, the declining craving-related activations (habituation) and increasing inhibition-associated activations (sensitization) during the task duration suggest the gradual recruitment of response inhibitory processes and a concurrent habituation to drug cues in areas with temporally-dynamic methamphetamine-related response inhibition. Furthermore, temporally dynamic cue-reactivity and response inhibition were correlated with behavioral and clinical measures such as the severity of methamphetamine use and craving, impulsivity and inhibitory task performance. This exploratory study demonstrates the time-variance of the neural activations undergirding cue-reactivity, response inhibition, and response inhibition during exposure to drug cues, and suggests a method to assess this dynamic interplay. Analyses that can capture temporal fluctuations in the neural substrates of drug cue-reactivity and response inhibition may prove useful for biomarker development by revealing the rate and pattern of sensitization and habituation processes, and may inform mixed cue-exposure intervention paradigms which could promote habituation to drug cues and sensitization in inhibitory control regions.

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study.

Growing evidence indicates that disruptions in the brain's functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and ß bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images.

BACKGROUND: Brain tumor extraction from magnetic resonance (MR) images is challenging due to variations in the location, shape, size and intensity of tumors. Manual delineation of brain tumors from MR images is time-consuming and prone to human errors. METHOD: In this paper, we present a method for automatic tumor extraction from multimodal MR images. Brain tumors are first detected using k-means clustering. A morphological region-based active contour model is then used for tumor extraction using an initial contour defined based on the boundary of the detected brain tumor regions. The contour evolution for tumor extraction was performed using successive application of morphological operators. In our model, a Gaussian distribution was used to model local image intensities. The spatial correlation between neighboring voxels was also modeled using Markov random field. RESULTS: The proposed method was evaluated on BraTS 2013 dataset including patients with high-grade and low-grade tumors. In comparison with other active contour based methods, the proposed method yielded better performance on tumor segmentation with mean Dice similarity coefficients of 0.9179 ( ±â€¯0.025) and 0.8910 ( ±â€¯0.042) obtained on high-grade and low-grade tumors, respectively. CONCLUSION: The proposed method achieved higher accuracies for brain tumor extraction in comparison to other contour-based methods.

Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder.

Attention deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity (dFC) between 113 children with inattentive (46 ADHDI) and combined (67 ADHDC) ADHD and 76 typically developing (TD) children using resting-state functional MRI data. For dynamic connectivity analysis, the data were first decomposed into 100 independent components, among which 88 were classified into eight well-known resting-state networks (RSNs). Three discrete FC states were then identified using k-means clustering and used to estimate transition probabilities between states in both patient and control groups using a hidden Markov model. Our results showed state-dependent alterations in intra and inter-network connectivity in both ADHD subtypes in comparison with TD. Spending less time than healthy controls in state 1, both ADHDIand ADHDCwere characterized with weaker intra-hemispheric connectivity with functional asymmetries. In this state, ADHDIfurther showed weaker inter-hemispheric connectivity. The patients spent more time in state 2, exhibiting characteristic abnormalities in corticosubcortical and corticocerebellar connectivity. In state 3, a less frequently state observed across the ADHD and TD children, ADHDCwas differentiated from ADHDIby significant alterations in FC between bilateral temporal regions and other brain areas in comparison with TD. Across all three states, several strategic brain regions, mostly bilateral, exhibited significant alterations in both static functional connectivity (sFC) and dFC in the ADHD groups compared to TD, including inferior, middle and superior temporal gyri, middle frontal gyri, insula, anterior cingulum cortex, precuneus, calcarine, fusiform, superior motor area, and cerebellum. Our results show distributed abnormalities in sFC and dFC between different large-scale RSNs including cortical and subcortical regions in both ADHD subtypes compared to TD. Our findings show that the dynamic changes in brain FC can better explain the underlying pathophysiology of ADHD such as deficits in visual cognition, attention, memory and emotion processing, and cognitive and motor control.

Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks.

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization.

Melanoma is one of the deadliest types of skin cancer with increasing incidence. The most definitive diagnosis method is the histopathological examination of the tissue sample. In this paper, a melanoma detection algorithm is proposed based on decision-level fusion and a Hidden Markov Model (HMM), whose parameters are optimized using Expectation Maximization (EM) and asymmetric analysis. The texture heterogeneity of the samples is determined using asymmetric analysis. A fusion-based HMM classifier trained using EM is introduced. For this purpose, a novel texture feature is extracted based on two local binary patterns, namely local difference pattern (LDP) and statistical histogram features of the microscopic image. Extensive experiments demonstrate that the proposed melanoma detection algorithm yields a total error of less than 0.04%.

A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images.

The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most commonly used method in predicting tumor proliferation speed is the counting of mitotic figures in Hematoxylin and Eosin (H&E) histological slides. Manual mitosis counting is known to suffer from reproducibility problems. This paper presents a fully automated system for tumor proliferation prediction from whole slide images via mitosis counting. First, by considering the epithelial tissue as mitosis activity regions, we build a deep-learning-based region of interest detection method to select the high mitosis activity regions from whole slide images. Second, we learned a set of deep neural networks to detect mitosis detection from selected areas. The proposed mitosis detection system is designed to effectively overcome the mitosis detection challenges by two novel deep preprocessing and two-step hard negative mining approaches. Third, we trained a Support Vector Machine (SVM) classifier to predict the final tumor proliferation score. The proposed method was evaluated on the dataset of the Tumor Proliferation Assessment Challenge (TUPAC16) and achieved a 73.81 % F-measure and 0.612 weighted kappa score, respectively, outperforming all previous approaches significantly. Experimental results demonstrate that the proposed system considerably improves the tumor proliferation prediction accuracy and provides a reliable automated tool to support health care make-decisions.

Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder.

OBJECTIVE: This study investigated age-dependent and subtype-related alterations in electroencephalography (EEG) power spectra and current source densities (CSD) in children with attention deficit and hyperactivity disorder (ADHD). METHODS: We performed spectral and cortical source (exact low-resolution electromagnetic tomography, eLORETA) analyses using resting state EEG recordings from 40 children (8-16 years) with combined and inattentive subtypes of ADHD and 41 age-matched healthy controls (HC). Group differences in EEG spectra and CSD were investigated at each scalp location, voxel and cortical region in delta, theta, alpha and beta bands. We also explored associations between topographic changes in EEG power and CSD and age. RESULTS: Compared to healthy controls, combined ADHD subtype was characterized with significantly increased diffuse theta/beta power ratios (TBR) with a widespread decrease in beta CSD. Inattentive ADHD subtype presented increased TBR in all brain regions except in posterior areas with a global increase in theta source power. In both ADHD and HC, older age groups showed significantly lower delta source power and TBR and higher alpha and beta source power than younger age groups. Compared to HC, ADHD was characterized with increases in theta fronto-central and temporal source power with increasing age. CONCLUSIONS: Our results confirm that TBR can be used as a neurophysiological biomarker to differentiate ADHD from healthy children at both the source and sensor levels. SIGNIFICANCE: Our findings emphasize the importance of performing the source imaging analysis in order to better characterize age-related changes in resting-state EEG activity in ADHD and controls.

Fast T2 mapping using multi-echo spin-echo MRI: A linear order approach.

PURPOSE: Multi-echo spin-echo sequence is commonly used for T2 mapping. The estimated values using conventional exponential fit, however, are hampered by stimulated and indirect echoes leading to overestimation of T2 . Here, we present fast analysis of multi-echo spin-echo (FAMESE) as a novel approach to decrease the complexity of the search space, which leads to accelerated measurement of T2 . METHODS: We developed FAMESE based on mathematical analysis of the Bloch equations in which the search space dimension decreased to only one. Then, we tested it in both phantom and human brain. Bland-Altman plot was used to assess the agreement between the estimated T2 values from FAMESE and the ones estimated from single-echo spin-echo sequence. The reliability of FAMESE was assessed by intraclass correlation coefficients. In addition, we investigated the noise stability of the method in synthetic and experimental data. RESULTS: In both phantom and healthy participants, FAMESE provided accelerated and SNR-resistant T2 maps. The FAMESE had a very good agreement with the single-echo spin echo for the whole range of T2 values. The intraclass correlation coefficient values for FAMESE were excellent (ie, 0.9998 and 0.9860 < intraclass correlation coefficient < 0.9942 for the phantom and humans, respectively). CONCLUSION: Our developed method FAMESE could be considered as a candidate for rapid T2 mapping with a clinically feasible scan time.

Neonatal EEG sleep stage classification based on deep learning and HMM.

OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. APPROACH: The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies. MAIN RESULTS: Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71-0.76 and 78.9%-82.4% using the KFCV and LOOCV strategies, respectively. SIGNIFICANCE: The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model.

OBJECTIVE: Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. METHOD: In this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies. RESULTS: Using a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4-87.4%) and (77.6-80.4%) with Kappa values in the range of 0.7-0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method. COMPARISON WITH EXISTING METHOD(S): Our method outperformed the existing methods for all multi-stage classification. CONCLUSIONS: The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.

Morphological active contour driven by local and global intensity fitting for spinal cord segmentation from MR images.

BACKGROUND: Spinal cord (SC) segmentation from magnetic resonance (MR) images can be used to study neurological disorders and facilitates group analysis. Variation of intensity inhomogeneity and small cross section of SC are difficulties that restrict automizing SC segmentation. NEW METHODS: In this paper we present a method for accurate SC segmentation from MR images. The proposed morphological local global intensity fitting model (MLGIF) is based on region based morphological active contour model that utilizes local and global information. The local information is obtained using local morphology fitting and has been embedded into region based active contour to deal with images intensity inhomogeneity and variable contrast levels between SC and the cerebrospinal fluid. The contour evolution has been performed using successive application of a set of morphological operators. RESULTS: The proposed method has been validated on 28 T1-weighted and 29 T2-weighted MR images and simulated MR images with different noise levels. Assessment of the results shows the accuracy of the proposed method for SC segmentation. COMPARISON TO EXISTING METHOD(S): The proposed MLGIF method was comparable with existing SC segmentation methods. Between segmented images and corresponding ground truth images, the mean DICE similarity coefficient, mean conformity coefficient and mean Hausdorff distance were 0.90 (092), 0.8 (0.83) and 0.85 mm (0.70 mm), respectively, for T1(T2)-weighted images. CONCLUSION: The MLGIF model was proposed to achieve a robust method to deal with intensity inhomogeneity and lack of contrast between SC and surrounding tissues. Moreover, accuracy and less sensitivity to initial curve were seen.

An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images.

This paper proposes an automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different laboratories. Therefore, this paper introduces an automatic robust method to segment leukocyte from blood microscopic images. The proposed robust segmentation technique was designed based on the fact that if background and erythrocytes could be removed from the blood microscopic image, the remainder area will indicate leukocyte candidate regions. A new set of features based on hematologist visual criteria for the recognition of malignant leukocytes in blood samples comprising shape, color, and LBP-based texture features are extracted. Two new ensemble classifiers are proposed for healthy and malignant leukocytes classification which each of them is highly effective in different levels of analysis. Experimental results demonstrate that the proposed approach effectively segments leukocytes from various types of blood microscopic images. The proposed method performs better than other available methods in terms of robustness and accuracy. The final accuracy rate achieved by the proposed method is 98.10% in cell level. To the best of our knowledge, the image level test for acute lymphoblastic leukemia (ALL) recognition was performed on the proposed system for the first time that achieves the best accuracy rate of 89.81%.

Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy.

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.

Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan-Converted Ultrasound Images.

Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify normal and fatty liver, as well as discriminate three stages of fatty liver in ultrasound images. This study is performed with 129 subjects including 28 normal, 47 steatosis, 42 fibrosis, and 12 cirrhosis images. The proposed approach uses back-scan conversion of ultrasound sector images and is based on a hierarchical classification. The proposed algorithm is performed in two parts. The first part selects the optimum regions of interest from the focal zone of the back-scan-converted ultrasound images. In the second part, discrimination between normal and fatty liver is performed and then steatosis, fibrosis, and cirrhosis are classified in a hierarchical basis. The wavelet packet transform and gray-level co-occurrence matrix are used to obtain a number of statistical features. A support vector machine classifier is used to discriminate between normal and fatty liver, and stage fatty cases. The results of the proposed scheme clearly illustrate the efficiency of this system with overall accuracy of 94.91% and also specificity of more than 90%.

An improved method for liver diseases detection by ultrasound image analysis.

Ultrasound imaging is a popular and noninvasive tool frequently used in the diagnoses of liver diseases. A system to characterize normal, fatty and heterogeneous liver, using textural analysis of liver Ultrasound images, is proposed in this paper. The proposed approach is able to select the optimum regions of interest of the liver images. These optimum regions of interests are analyzed by two level wavelet packet transform to extract some statistical features, namely, median, standard deviation, and interquartile range. Discrimination between heterogeneous, fatty and normal livers is performed in a hierarchical approach in the classification stage. This stage, first, classifies focal and diffused livers and then distinguishes between fatty and normal ones. Support vector machine and k-nearest neighbor classifiers have been used to classify the images into three groups, and their performance is compared. The Support vector machine classifier outperformed the compared classifier, attaining an overall accuracy of 97.9%, with a sensitivity of 100%, 100% and 95.1% for the heterogeneous, fatty and normal class, respectively. The Acc obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists and experts in liver diseases interpretation.

A Novel CAD System for Mitosis detection Using Histopathology Slide Images.

Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.