A feature extraction approach based on color is firstly proposed. Exploiting color histogram of an image, we can obtain distribution of different colors in images. Then we employ minimum distance classifier based on a new distance criterion to judge status of regions. In this section, we also validate benefits of WCE image enhancement to the proposed CAD system. / Finally, we propose a new approach of chrominance moment as another kind of feature to discriminate normal regions from abnormal regions, which makes full use of Tchebichef polynomials and HSI color space. This new feature extraction scheme preserves illumination invariance without numerical approximation. / In conclusion, this thesis investigates several major and challenging problems such as WCE images enhancement and feature extractions in CAD for WCE images, and proposes several novel schemes to solve those problems. Extensive experiments are reported to demonstrate effectiveness of the proposed algorithms. / Next, we investigate automatic diseases detection for WCE images to partially solve the second problem. In this part we explore different features that are suitable for detection of diseases from three viewpoints, i.e., color, texture and chromaticity, because clinicians mainly use these clues to diagnose. At the same time, we introduce their corresponding classifiers. / We further advance a new texture feature extraction method, curvelet based local binary pattern, to detect abnormal regions in WCE images. This method takes advantage of curvelet transform and local binary pattern to describe textural features of WCE images. / Wireless capsule endoscopy (WCE) is a state-of-the-art technology to diagnose gastrointestinal (GI) tract diseases without invasiveness. However, there exist two major problems concerning WCE images. One problem is that many images for diagnosis have rather low contrast and are noisy, which causes difficulties to diagnosis and also to computer-aided detection, so it is necessary to enhance these images. The other one is that the viewing process of video data per examination is very time consuming because of the great amount of video data. If we can use computerized methods to help the physicians detect some abnormal regions in WCE images, it will certainly reduce the burden of physicians. Focusing on these two goals, this thesis mainly studies some main challenging problems in computer-aided diagnosis (CAD) system for WCE images. To solve the first problem, we put forward an adaptive curvature strength diffusion method to enhance WCE images. Based on local characteristics analysis of WCE images, we propose a new concept of curvature strength. Then, we employ curvature strength diffusion to enhance WCE images with an adaptive choice of conductance parameter. Finally, we extend the curvature strength diffusion to color space since WCE images are color images. / Li, Baopu. / Adviser: Max Q. H. Meng. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3640. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 126-150). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344303 |
| Date | January 2008 |
| Contributors | Li, Baopu., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xv, 153 leaves : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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