Mehdi Hassan

Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering

Disease diagnosis based on ultrasound imaging is popular because of its non-invasive nature. However, ultrasound imaging system produces low quality images due to the presence of spackle noise and wave interferences. This shortcoming requires a considerable effort from experts to diagnose a disease from the carotid artery ultrasound images. Image segmentation is one of the techniques, which can help efficiently in diagnosing a disease from the carotid artery ultrasound images. Most of the pixels in an image are highly correlated. Considering the spatial information of surrounding pixels in the process of image segmentation may further improve the results. When data is highly correlated, one pixel may belong to more than one clusters with different degree of membership. In this paper, we present an image segmentation technique namely improved spatial fuzzy c-means and an ensemble clustering approach for carotid artery ultrasound images to identify the presence of plaque. Spatial, wavelets and gray level co-occurrence matrix (GLCM) features are extracted from carotid artery ultrasound images. Redundant and less important features are removed from the features set using genetic search process. Finally, segmentation process is performed on optimal or reduced features. Ensemble clustering with reduced feature set outperforms with respect to segmentation time as well as clustering accuracy. Intima-media thickness (IMT) is measured from the images segmented by the proposed approach. Based on IMT measured values, Multi-Layer Back-Propagation Neural Networks (MLBPNN) is used to classify the images into normal or abnormal. Experimental results show the learning capability of MLBPNN classifier and validate the effectiveness of our proposed technique. The proposed approach of segmentation and classification of carotid artery ultrasound images seems to be very useful for detection of plaque in carotid artery.

Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images

In this paper, a robust method is proposed for segmentation of medical images by exploiting the concept of information gain. Medical images contain inherent noise due to imaging equipment, operating environment and patient movement during image acquisition. A robust medical image segmentation technique is thus inevitable for accurate results in subsequent stages. The clustering technique proposed in this work updates fuzzy membership values and cluster centroids based on information gain computed from the local neighborhood of a pixel. The proposed approach is less sensitive to noise and produces homogeneous clustering. Experiments are performed on medical and non-medical images and results are compared with state of the art segmentation approaches. Analysis of visual and quantitative results verifies that the proposed approach outperforms other techniques both on noisy and noise free images. Furthermore, the proposed technique is used to segment a dataset of 300 real carotid artery ultrasound images. A decision system for plaque detection in the carotid artery is then proposed. Intima media thickness (IMT) is measured from the segmented images produced by the proposed approach. A feature vector based on IMT values is constructed for making decision about the presence of plaque in carotid artery using probabilistic neural network (PNN). The proposed decision system detects plaque in carotid artery images with high accuracy. Finally, effect of the proposed segmentation technique has also been investigated on classification of carotid artery ultrasound images.

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view these facts, image alignment is used as a preprocessing step to align the carotid artery ultrasound images. In our experimental study, we exploit intima–media thickness (IMT) measurement to detect the presence of plaque in the artery. Support vector machine (SVM) classification is employed using these segmented images to distinguish the normal and diseased artery images. IMT measurement is used to form the feature vector. Our proposed approach segments the carotid artery images in an automatic way and further classifies them using SVM. Experimental results show the learning capability of SVM classifier and validate the usefulness of our proposed approach. Further, the proposed approach needs minimum interaction from a user for an early detection of plaque in carotid artery. Regarding the usefulness of the proposed approach in healthcare, it can be effectively used in remote areas as a preliminary clinical step even in the absence of highly skilled radiologists.

Automatic Segmentation and Decision Making of Carotid Artery Ultrasound Images

Disease diagnostics based on medical imaging is getting popularity day after day. Presence of the arthrosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present automatic image segmentation and decision making technique for carotid artery ultrasound images based on active contour approach. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Intima-media thickness (IMT) measurement is used to form a feature vector for classification. Five different features are extracted from IMT measured values. K-nearest neighbors (KNN) classifier is applied for classification of the images. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Decision is made based on the feature vector obtained from IMT values. Using the proposed approach we have obtained 98.30% classification accuracy. Our proposed approach successfully segment and classify the carotid artery images in an automated way to help radiologists. Obtained results show the effectiveness of the proposed approach.

Neuro fuzzy and punctual kriging based filter for image restoration

In this paper, we present a hybrid, image restoration approach. The proposed approach combines the geostatistical interpolation of punctual kriging, artificial neural networks (ANNs), and fuzzy logic based approaches. Images degraded with Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that needs kriging. Three fuzzy systems are employed. Both type-I and type-II fuzzy sets in addition with neuro fuzzy classifier (NFC) have been used for the detection of noisy pixels. To avoid edge pixels, a post processing technique is used to check the edge pixel connectivity up to lag 5. If the pixel under consideration is an edge pixel, it is excluded from the fuzzy map and thus not estimated. The concept of punctual kriging is then used to estimate the intensity of a noisy pixel. ANN is employed to minimize the cost function of the kriging based pixel intensity estimation procedure. ANN, in contrast to analytical methodologies, avoids both matrix inversion failure and negative weights problems. Image restoration performance based comparison has been made against adaptive Weiner filter and existing fuzzy kriging approaches. Experimental results using 450 images are used to validate the effectiveness of the proposed approach. Different image quality measures are used to compare the efficacy of the proposed NFC and fuzzy type-II approaches for detecting noisy pixels in conjunction with ANN and kriging based estimation.

Medical image segmentation employing information gain and fuzzy c-means algorithm

In this paper, we proposed a new approach for image clustering to address the adverse effects of noise presented in the images. In particular, the concept of information gain has been incorporated into classical fuzzy c-means (FCM) algorithm in order to develop a robust clustering method. FCM is associated with high sensitivity to noise and produces non-homogenous clustering. To induce robustness to noise, the new clustering technique updates fuzzy membership values and cluster centroids based on information gain. The proposed method produces more homogeneous clustering and its performance can be verified at noisy and noise free images. Experiments have been performed on synthetic, CT liver images and compared with those of classical FCM and one of its robust variants. Moreover, the proposed algorithm has been validated on a data set of 30 carotid artery ultrasound images. Visual inspection of segmented images and clustering quality measures confirm that the proposed approach outperforms other clustering algorithms in comparison. Quantitative measures, in terms of PC and CE, also lead to similar conclusion. Hence, the proposed algorithm is robust to noise and produces homogenous clustering.

Ensemble Sparse Classification of Colon Cancer

Automated colon cancer detection helps get rid of the slow and laborious process of manual examination of histopathological tissue specimens using microscope, and provides a reliable second opinion to the histopathologists. Therefore, automated colon cancer detection has been the focus of research community in the past two decades, and researchers have proposed various automatic colon cancer detection systems. Most of the existing colon cancer detection systems extract features and then construct single classifier to perform classification. However, the small sample size problem and especially the noise in neuroimaging data makes it challenging to achieve good classification results by training only one global classifier. In this work, we propose a local patch based ensemble method instead of building a single global classifier. The proposed method builds multiple individual weak classifiers based on the different subsets of local patches, and then combines the output of weak classifiers for more accurate and robust classification. In particular, Haralick and Local Binary Patterns (LBP) features are extracted from pre-processed colon biopsy images, and images are partitioned into smaller fixed size patches. Features of the random subsets of patches are combined to train weak classifier. Several kernels of SVM such as linear, RBF and sigmoid are used as weak classifiers. Later, the output of various weak classifiers is combined to get the final classification results. The proposed subspace ensemble classification method yields better results compared to one global classifier in all the three cases (linear, RBF, sigmoid) in terms of various performance measures such as accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curves, Matthews correlation coefficient, and Area Under the Curve (AUC), however, classification performance is slightly better for ensemble of RBF kernel. The proposed method has also yielded better performance compared to existing techniques on a colon cancer dataset of 174 subjects.

Utilizing distinct terms for proximity and phrases in the document for better information retrieval

The rapid increase in web data has led the users to search the web for information. Users want their queries to be more relevant to the documents. For this purpose, the idea of proximity is utilized where close query terms in the document show high proximity and hence the document is likely to be more relevant. But almost every retrieval function (e.g., BM25) uses bag-of-words technique, thus ignoring the importance of proximity. In this paper, we find such short sentences (sub-sentences) or group of words that are made by distinct words or terms on their first occurrence in the documents to calculate the proximity and subsequently incorporate it into a retrieval function to improve the ranking of the documents. We have shown that there are significant numbers of short sentences formed by distinct words that can be used to exploit proximity and phrase search. Furthermore, turning the non-positional (record level) inverted index into partial-positional inverted index by utilizing distinct terms (UDT), the UDT partial-positional inverted index has been separated from the full positional (word level) inverted index to calculate proximity among the distinct terms efficiently. As calculating proximity with full positional inverted index is complex and computationally expensive, the proximity with the UDT partial-positional inverted index can be computed easily and efficiently. Experiments on various data sets have shown that the proposed approach has improved the precision of the documents.

Worldwide Impact of BT Cotton: Implications for Agricultural Research in Pakistan

Since 2010 many BT varieties have been approved in Pakistan and also have been adopted by the farmers commercially as well. Not only is the adoption and production lower than the other BT adopter countries of the world but also development plans are slower. Pakistan needs more resistant varieties that should be susceptible to agronomic conditions of the country and safer for the environment. Effort has been made in this publication to explain the BT cotton trends in other countries of the world and also originated the need of modifications in Pakistan. More importantly, this manuscript explores the need on how BT can be effectively utilized in Pakistan. Audiences for this manuscript involve research department, extension agents along with public and private sectors and the end users (cotton growers). All researchers, policy formulators, donor agencies, authoritative groups and farmers have a responsibility to fully consider the diversity of BT.