COMPOSITE KERNEL FEATURE ANALYSIS FOR CANCER CLASSIFICATION

Computed tomographic (CT) colonography, or virtual colonoscopy, is a promising technique for screening colorectal cancers by use of CT scans of the colon. Current CT technology allows a single image set of the colon to be acquired in 10-20 seconds, which translates into an easier, more comfortable examination than is available with other screening tests. Currently, however, interpretation of an entire CT colonography examination is time-consuming, and the reader performance for polyp detection varies substantially. To overcome these difficulties while providing a high detection performance of polyps, researchers are developing computer-aided detection (CAD) schemes that automatically detect suspicious lesions in CT colonography images. The overall goal of this study is to achieve a high performance in the detection of polyps on CT colonographic images by effectively incorporating an appearance-based object recognition approaches into a model-based CAD scheme. Our studies are focused in developing a fast kernel feature analysis that can efficiently differentiate polyps from false positives and thus improve the detection performance of polyps. We have developed a novel method of selecting kernel functions that are appropriate for the given data set and then use their linear combination in the construction of Kernel Gram matrix which can then used for efficient reconstruction of feature space. The main contribution of this work lies in providing a Composite kernel Matrix that involves appearance-based approach to improve kernel feature analysis for the classification of texture-based features. We evaluated our proposed kernel feature analysis on texture-based features that were extracted from the polyp candidates generated by our shape-based CAD scheme.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2010

Author dc:creator

• Myla, Sindhu

Contributors dc:contributor

• Yuichi Motai

• Electrical and Computer Engineering
• Engineering

Statement dc:rights

• © The Author

Identifier

https://scholarscompass.vcu.edu/etd/54

OAI identifier oai:identifier

oai:scholarscompass.vcu.edu:etd-1053