English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6480/11652
Visitors : 20640166      Online Users : 110
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11736

Title: Using a fuzzy engine and complete set of features for hepatic diseases diagnosis: Integrating contrast and non-contrast CT images
Authors: Chen, E.-L.;Chung, Yi-Nung;Chung, P.-C.;Tsai, H.-M.;Huang, Y.-S.
Contributors: 電機工程學系
Keywords: Contrast-enhanced CT;Fuzzy-diagnosis engine;Hepatic disease diagnosis;Non-contract CT
Date: 2001-08
Issue Date: 2012-07-02T02:06:18Z
Publisher: World Scientific Publishing Co.
Abstract: In the diagnosis of hepatic diseases, "Contrast-Enhanced Computerized Tomography" (CECT) and "Non-Contrast CT" (NCT) are usually simultaneously adopted. In this paper, a system consisting of a fuzzy diagnosis engine and a feature quantizer, which extracts hepatic features from CECT and NCT images is proposed for assisting hepatic disease diagnosis. Compared with existing methods this paper differs in two folds. First a more complete features set composed of not only lesion textures, but also lesion morphological structure and lesion contrast to normal tissues is used. These features are described through mathematical models built inside the feature quantizer and served as the input of fuzzy diagnosis engine. Second, because of the use of the fuzzy diagnosis engine, the system is intrinsically with the capability of storing rules and may infer and adapt its rules according to learning data. Furthermore, uncertainty associated with disease diagnosis can be appropriately taken into considerations. The system has been tested using 131 sets of image data, which are to be classified into 4 types of diseases: liver cyst, hepatoma, cavernous hemagioma and metastatic liver tumor. Experimental results indicate that among these test data 78% of them are accurately classified as one type, while the remaining 22% of data are classified as more than one types of diseases. Even so, within these 22% of multiple-classified data, the correct type is always included in the output in each test, showing a promise of the system.
Relation: Biomedical Engineering - Applications, Basis and Communications, 13(4): 159-167
Appears in Collections:[電機工程學系] 期刊論文

Files in This Item:

File SizeFormat
index.html0KbHTML386View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback