Comparative evaluation of feature extraction techniques for automatic disease detection in capsule endoscopy images = 특징 추출 기법을 적용한 캡슐 내시경 영상의 자동 질병 분류 시스템

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A capsule endoscopy abnormal detection system is used to classify the abnormal images in wireless capsule endoscopy videos. Wireless Capsule Endoscopy is a relatively new technology allowing doctor to view most of the small intestine. The research has been attempted to automatically find abnormal regions to reduce doctors` time needed to analyze the videos. The proposed system uses multi-resolution features, which is compatible with various disease sizes of capsule endoscopy. ICA, PCA and NMF are applied to four different window size based approach to extract the part-based and holistic feature representation of the abnormal pattern. The classification of abnormal windows for each resolution is carried out by means of a SVM classifier, and Single-Layer Perceptron combines the 4 SVM results. After that, feature extraction method with maximum performance is selected and frame based decision is brought out. The ROC curves are used to exhibit the true positive rate (sensitivity) versus false positive rate (1-specificity) of the classfier. The proposed algorithm shows high accuracy of abnormality detection with signi_cant time reduction to inspect videos of WCE with a minimal loss of performance.
Advisors
Lee, Soo-Youngresearcher이수영researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2008
Identifier
296160/325007  / 020063139
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2008.2, [ vii, 49 p. ]

Keywords

Capsule endoscopy; Unsupervised feature; Medical image processing; Classification; Learning; 캡슐내시경; 특징 추출 기법; 의료 영상 처리; 분류; 학습; Capsule endoscopy; Unsupervised feature; Medical image processing; Classification; Learning; 캡슐내시경; 특징 추출 기법; 의료 영상 처리; 분류; 학습

URI
http://hdl.handle.net/10203/27147
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=296160&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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