USING A FUZZY ENGINE AND COMPLETE SET OF FEATURES FOR HEPATIC DISEASES DIAGNOSIS: INTEGRATING CONTRAST AND NON-CONTRAST CT IMAGES
| Autor: | Yi-Shiuan Huang, Pau-Choo Chung, Horng-Ming Tsai, Yi-Nung Chung, E-Liang Chen |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Liver tumor
business.industry media_common.quotation_subject Biomedical Engineering Biophysics Bioengineering Pattern recognition Fuzzy control system medicine.disease Fuzzy logic Set (abstract data type) Feature (computer vision) Medicine Contrast (vision) Artificial intelligence Tomography business Test data media_common Biomedical engineering |
| Zdroj: | Biomedical Engineering: Applications, Basis and Communications. 13:159-167 |
| ISSN: | 1793-7132 1016-2372 |
| DOI: | 10.4015/s1016237201000200 |
| Popis: | 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 feature 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. |
| Databáze: | OpenAIRE |
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