Saima Rathore

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

ABSTRACT Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimers disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging‐based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub‐categorized based on features extracted as a post‐processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG‐PET) (metabolic rate of cerebral glucose), and v) amyloid‐PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions. HIGHLIGHTSWe reviewed Alzheimers disease neuroimaging‐based classification studies.We covered structural MRI, fMRI, DTI, amyloid‐PET, FDG‐PET, and multimodalities.The reported studies were validated through appropriate cross‐validation.We categorized the studies based on feature extraction methods.We discussed challenges, disparities in experimental conditions and future directions.

A Recent Survey on Colon Cancer Detection Techniques

Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.

Ensemble classification of colon biopsy images based on information rich hybrid features

In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the performance of CBIC on original and reduced feature sets.

GECC: gene expression based ensemble classification of colon samples

Gene expression deviates from its normal composition in case a patient has cancer. This variation can be used as an effective tool to find cancer. In this study, we propose a novel gene expressions based colon classification scheme (GECC) that exploits the variations in gene expressions for classifying colon gene samples into normal and malignant classes. Novelty of GECC is in two complementary ways. First, to cater overwhelmingly larger size of gene based data sets, various feature extraction strategies, like, chi-square, F-Score, principal component analysis (PCA) and minimum redundancy and maximum relevancy (mRMR) have been employed, which select discriminative genes amongst a set of genes. Second, a majority voting based ensemble of support vector machine (SVM) has been proposed to classify the given gene based samples. Previously, individual SVM models have been used for colon classification, however, their performance is limited. In this research study, we propose an SVM-ensemble based new approach for gene based classification of colon, wherein the individual SVM models are constructed through the learning of different SVM kernels, like, linear, polynomial, radial basis function (RBF), and sigmoid. The predicted results of individual models are combined through majority voting. In this way, the combined decision space becomes more discriminative. The proposed technique has been tested on four colon, and several other binary-class gene expression data sets, and improved performance has been achieved compared to previously reported gene based colon cancer detection techniques. The computational time required for the training and testing of 208 × 5,851 data set has been 591.01 and 0.019 s, respectively.

Texture Analysis for Liver Segmentation and Classification: A Survey

Texture is a combination of repeated patterns with regular/irregular frequency. It can only be visualized but hard to describe in words. Liver structure exhibit similar behavior, it has maximum disparity in intensity texture inside and along boundary which serves as a major problem in its segmentation and classification. Problem gets more complicated when one applies simple segmentation techniques without considering variation in intensity texture. The problem of representing liver texture is solved by encoding it in terms of certain parameters for texture analysis. Numerous textural analysis techniques have been devised for liver classification over the years some of which work equally work well for most of the imaging modalities. Here, we attempt to summarize the efficacy of textural analysis techniques devised for Computed Tomography (CT), Ultrasound and some other imaging modalities like Magnetic Resonance Imaging (MRI), in terms of well-known performance metrics.

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space.

Novel structural descriptors for automated colon cancer detection and grading

The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer. However, the process is subjective and leads to significant inter/intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists. Therefore, a reliable computer-aided technique, which can automatically classify normal and malignant colon samples, and determine grades of malignant samples, is required. In this paper, we propose a novel colon cancer diagnostic (CCD) system, which initially classifies colon biopsy images into normal and malignant classes, and then automatically determines the grades of colon cancer for malignant images. To this end, various novel structural descriptors, which mathematically model and quantify the variation among the structure of normal colon tissues and malignant tissues of various cancer grades, have been employed. Radial basis function (RBF) kernel of support vector machines (SVM) has been employed as classifier in order to classify/grade colon samples based on these descriptors. The proposed system has been tested on 92 malignant and 82 normal colon biopsy images. The classification performance has been measured in terms of various performance measures, and quite promising performance has been observed. Compared with previous techniques, the proposed system has demonstrated better cancer detection (classification accuracy=95.40%) and grading (classification accuracy=93.47%) capability. Therefore, the proposed CCD system can provide a reliable second opinion to the histopathologists.

Classification of colon biopsy images based on novel structural features

Microscopic analysis of colon biopsy samples is a common medical practice for identifying colon cancer. However, the process is subjective, and leads to significant inter-observer/intra-observer variability. Further, pathologists have to examine many biopsy samples per day, therefore, factors such as expertise and workload of the histopathologists also affect the diagnosis. These limitations of the manual process result in the need of a computer-aided diagnostic system, which can help the histopathologists in accurately determining cancer. Image classification is one of such computer-aided techniques, which can help in efficiently identifying normal and malignant colon biopsy samples without the need of subjective involvement of histopathologists. In this work, we propose a colon biopsy image classification technique, wherein two novel structural feature types, namely, white run-length features and percentage cluster area features have been introduced These features are calculated for each colon biopsy image. The features are reduced using principal component analysis (PCA). The original and the reduced feature sets are then given as input to random forest, rotation forest, and rotation boost classifiers for classification of images into normal and malignant categories. The proposed technique has been evaluated on 174 colon biopsy images, and promising performance has been observed in terms of various well-known performance measures such as accuracy, sensitivity and specificity. The proposed technique has also been proven to be better compared to previously published techniques in the experimental section. Further, performance of the classifiers has been evaluated using ROC curves, and area under the curve (AUC). It has been observed that rotation boost classifier in combination with PCA based feature selection has shown better results in classification compared to other classifiers.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

A novel approach for ensemble clustering of colon biopsy images

Colon cancer diagnosis based on microscopic analysis of biopsy sample is a common medical practice. However, the process is subjective, biased and leads to interobserver variability. Further, histopathologists have to analyze many biopsy samples per day. Therefore, factors such as tiredness, experience and workload of histopathologists also affect the diagnosis. These shortcomings require a supporting system, which can help the histopathologists in accurately determining cancer. Image segmentation is one of the techniques, which can help in efficiently segregating colon biopsy image into constituent regions, and accurately localizing the cancer. In this work, we propose a novel colon biopsy image segmentation technique, wherein segmentation has been posed as a classification problem. Local binary patterns (LTP), local ternary patters (LTP), and Haralick features are extracted for each pixel of colon biopsy images. Features are reduced using genetic algorithms and F-Score. Reduced features are given as input to random forest, rotation forest, and rotation boost classifiers for segregation of image into normal, malignant and connecting tissues components. The clustering performance has been evaluated using segmentation accuracy and Davies bouldin index (DBI). Performance of classifiers has also been evaluated using receiver operating characteristics (ROC) curves, and area under the curve (AUC). It is observed that rotation boost in combination with F-Score has shown better results in segmenting the images compared to other classifiers.