Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy

Image classification : a study in age-related macular degeneration screening

This thesis presents research work conducted in the field of image mining. More specifically, the work is directed at the employment of image classification techniques to classify images where features of interest are very difficult to distinguish. In this context, three distinct approaches to image classification are proposed. The first is founded on a time series based image representation, whereby each image is defined in terms of histograms that in turn are presented as "time series" curves. A Case Based Reasoning (CBR) mechanism, coupled with a Time Series Analysis (TSA) technique, is then applied to classify new "unseen" images. The second proposed approach uses statistical parameters that are extracted from the images either directly or indirectly. These parameters are then represented in a tabular form from which a classifier can be built on. The third is founded on a tree based representation, whereby a hierarchical decomposition technique is proposed. The images are successively decomposed into smaller segments until each segment describes a uniform set of features. The resulting tree structures allow for the application of weighted frequent sub-graph mining to identify feature vectors representing each image. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The presented evaluation, applied to all three approaches, is directed at the classification of retinal colour fundus images; the aim is to screen for an eye condition known as Age-related Macular Degeneration (AMD). Of all the approaches considered in this thesis, the tree based representation coupled with weighted frequent sub-graph mining produced the best performance. The evaluation also indicated that a sound foundation has been established for future potential AMD screening programmes

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition