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Robust Texture Features with Applications in Medical Imaging Open Access


Other title
Volumetric Texture
Robust Gradient Operator
Texture Classification
3D Texture
Robust Edge Aware Descriptor
Medical Imaging
Local Frequency Descriptor
Type of item
Degree grantor
University of Alberta
Author or creator
Maani, Rouzbeh
Supervisor and department
Yang, Yee Hong (Computing Science)
Kalra, Sanjay (Medicine)
Examining committee member and department
Zhao, Vicky (Electrical and Computer Engineering)
Yang, Yee Hong (Computing Science)
Boulanger, Pierre (Computing Science)
Ross, Mitchell (Radiology)
Kalra, Sanjay (Medicine)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Image texture is defined as visual patterns appearing in images. The powerful perceptive capability of texture features has made texture analysis a major research topic in computer vision and image processing. Texture features are used to detect defective products in factories, to understand human actions in surveillance systems, to identify people from biometric data (e.g., fingerprint, iris scan, and face photo), and to find abnormality in medical images. Indeed, many advanced applications take a direct or indirect advantage of texture analysis in their processing. An ideal texture feature should not only be discriminative but also be robust to imaging distortions. The developement of robust texture features is first motivated by applying texture analysis to Amyotrophic Lateral Sclerosis (ALS). ALS is a fatal neurodegenerative disease in which evidence of the disease is not perceptible in routine magnetic resonance images (MRI) of the brain even to a trained eye. Unlike brain tumors or multiple sclerosis, the lack of observable features possesses challenges to the detection and diagnosis of ALS. These challenges and the great need in the ALS research community to find a biomarker and to detect the patterns of degeneration in the brain have encouraged the author to study this disease using texture analysis. The results of this thesis suggest texture analysis is a potential biomarker for the disease and hence, open up new avenues towards understanding the disease. This thesis presents a useful approach for texture analysis of the brain. In contrast to the current methods, the proposed approach does not need a region of interest. It performs a voxel based texture analysis and provides a statistical map showing the regions in the brain statistically different between the groups of patients and healthy subjects. A Computer Aided Diagnosis (CAD) tool is developed for this purpose. This toolbox is called the Statistical MAp fRom Texture (SMART) and helps doctors make diagnoses and monitor the progression of diseases using texture analysis. Distortions and effects in real images (e.g., noise, illumination change, blurr effect) increase demand for developing robuts texture features. To address the robustness issues, a novel approach is presented called the Local Frequancy Descriptor (LFD). The LFD is the basis of several novel 2D and 3D texture features presented later in this thesis. It is also the basis of new image gradient operators for 2D and 3D images and a novel image matching method. All texture features, methods, and gradient operators defined based on the LFD show high accuracy and outperform the state-ofthe-art methods. In addition, they present remarkable robutness to imaging effects.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
R. Maani, Y. H. Yang, and S. Kalra. Voxel-based Texture Analysis of the Brain, PLOS ONE, To Appear.R. Maani, Y. H. Yang, and S. Kalra. Texture analysis of the brain in Amyotrophic Lateral Sclerosis. Poster presented at 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), June 16–20, Seattle, USA, 2013.R. Maani, S. Kalra, and Y. H. Yang. Noise robust rotation invariant features for texture classification. Pattern Recognition, 46(8):2103–2116, 2013.R. Maani, S. Kalra, and Y. H. Yang. Rotation invariant local frequency descriptors for texture classification. IEEE Transactions on Image Processing, 22(6):2409–2419, 2013.R. Maani, S. Kalra, and Y. H. Yang. Robust edge aware descriptor for image matching. In Proceedings of 12th Asian Conference on Computer Vision (ACCV), Nov 1–5, Singapore, Singapore, 2014.R. Maani, S. Kalra, and Y.H. Yang. Robust volumetric texture classification of magnetic resonance images of the brain using local frequency descriptor. IEEE Transactions on Image Processing, 23(10):4625–4636, 2014.

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