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Gradient Based Novel Texture Feature Extraction Methods For Texture Classification Open Access


Other title
Texture Analysis
Oriented Gradients
Co-occurrence matrix
Co-occurrence histograms of oriented gradients
Magnetic Resonance Images (MRI)
Type of item
Degree grantor
University of Alberta
Author or creator
E Elahi, G M Mashrur
Supervisor and department
Yang, Herbert (Computing Science)
Kalra, Sanjay (Department of Medicine, Biomedical Engineering and Computing Science)
Examining committee member and department
Ray, Nilanjan (Computing Science)
Yang, Herbert (Computing Science)
Kalra, Sanjay (Department of Medicine, Biomedical Engineering and Computing Science)
Department of Computing Science

Date accepted
Graduation date
2016-06:Fall 2016
Master of Science
Degree level
Texture analysis is a well-known research topic in computer vision and image processing and has many applications. For example, texture features in image classification have been shown to be useful. Texture features depend on representation, of which there are many methods. Among them, gradient-based methods have become popular in classification problems. One of the gradient based methods is Co-occurrence Histograms of Oriented Gradients (CoHOG) has been applied in many areas. CoHOG algorithm provides a unified description of both statistical and differential properties for a texture. But it discards some important texture information due to the use of sub-regions. In this thesis, based on the original CoHOG method, three novel feature extraction methods are proposed. All the methods use the whole image instead of sub-regions for feature calculation. Also we use a larger neighborhood size for the methods. In the first method, we use Sobel operators for gradient calculation named S-CoHOG. The second method uses Gaussian Derivative (GD) operators named GD-CoHOG and the third method named LFDG-CoHOG uses the Local Frequency Descriptor Gradient (LFDG) operators for gradient calculations. The extracted feature vector size is very large and classification using a large number of similar features does not provide the best results. In our proposed methods, only a minimum number of significant features are selected using area under the receiver operator characteristic (ROC) curve (AUC) thresholds. The selected features are used in a linear support vector machine (SVM) classifier to determine the classification accuracy. The classification results of the proposed methods are compared with that of the original CoHOG method using three well-known texture datasets. The classification results show that the proposed methods achieve the best classification results in all the datasets. The proposed methods are also evaluated for medical image classification. Three different cohort datasets of 2D Magnetic Resonance Images (MRI) are used along with a multicenter dataset to compare the classification results of the proposed methods with that of the gray level co-occurrence matrix (GLCM) method. The experimental results show that the proposed methods outperform that of the GLCM method.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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