Muhammad Aksam Iftikhar

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.

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.

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.

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.

Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Correlation tracker is computation intensive (if the search space or the template is large), has template drift problem, and may fail in case of fast maneuvering target, rapid changes in its appearance, occlusion suffered by it and clutter in the scene. Kalman filter can predict the target coordinates in the next frame, if the measurement vector is supplied to it by a correlation tracker. Thus, a relatively small search space can be determined where the probability of finding the target in the next frame is high. This way, the tracker can become fast and reject the clutter, which is outside the search space in the scene. However, if the tracker provides wrong measurement vector due to the clutter or the occlusion inside the search region, the efficacy of the filter is significantly deteriorated. Mean-shift tracker is fast and has shown good tracking results in the literature, but it may fail when the histograms of the target and the candidate region in the scene are similar (even when their appearance is different). In order to make the overall visual tracking framework robust to the mentioned problems, we propose to combine the three approaches heuristically, so that they may support each other for better tracking results. Furthermore, we present novel method for (1) appearance model updating which adapts the template according to rate of appearance change of target, (2) adaptive threshold for similarity measure which uses the variable threshold for each forthcoming image frame based on current frame peak similarity value, and (3) adaptive kernel size for fast mean-shift algorithm based on varying size of the target. Comparison with nine state-of-the-art tracking algorithms on eleven publically available standard dataset shows that the proposed algorithm outperforms the other algorithms in most of the cases.

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.

Robust brain MRI denoising and segmentation using enhanced non-local means algorithm

Image denoising is an integral component of many practical medical systems. Non‐local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non‐local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non‐local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c‐means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM‐filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1‐weighted (T1‐w) and T2‐weighted (T2‐w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2‐weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques.

Visual object tracking--classical and contemporary approaches

Visual object tracking (VOT) is an important subfield of computer vision. It has widespread application domains, and has been considered as an important part of surveillance and security system. VOA facilitates finding the position of target in image coordinates of video frames.While doing this, VOA also faces many challenges such as noise, clutter, occlusion, rapid change in object appearances, highly maneuvered (complex) object motion, illumination changes. In recent years, VOT has made significant progress due to availability of low-cost high-quality video cameras as well as fast computational resources, and many modern techniques have been proposed to handle the challenges faced by VOT. This article introduces the readers to 1) VOT and its applications in other domains, 2) different issues which arise in it, 3) various classical as well as contemporary approaches for object tracking, 4) evaluation methodologies for VOT, and 5) online resources, i.e., annotated datasets and source code available for various tracking techniques.

A novel approach for automatic gene selection and classification of gene based colon cancer datasets

Colon cancer heavily changes the composition of human genes (expressions). The deviation in the chemical composition of genes can be exploited to automatically diagnose colon cancer. The major challenge in the analysis of human gene based datasets is their large dimensionality. Therefore, efficient techniques are needed to select discerning genes. In this research article, we propose a novel classification technique that exploits the variations in gene expressions for classifying colon gene samples into normal and malignant classes, and quite intelligently tackles the larger dimensionality of gene based datasets. Previously individual feature selection techniques have been used for selection of discerning gene expressions, however, their performance is limited. In this research study, we propose a feed forward gene selection technique, wherein, two feature selection techniques are used one after the other. The genes selected by the first technique are fed as input to the second feature selection technique that selects genes from the given gene subset. The selected genes are then classified by using linear kernel of support vector machines (SVM). The feed forward approach of gene selection has shown improved performance. The proposed technique has been tested on three standard colon cancer datasets, and improved performance has been observed. It is observed that feed forward method of gene selection substantially reduces the size of gene based datasets, thereby reducing the computational time to a great extent. Performance of the proposed technique has also been compared with existing techniques of colon cancer diagnosis, and improved performance has been observed. Therefore, we hope that the proposed technique can be effectively used for diagnosis of colon cancer.