Using a fuzzy engine and complete set of features for hepatic diseases diagnosis: Integrating contrast and non-contrast CT images

E. L. Chen, Yi-Nung Chung, P. C. Chung, H. M. Tsai, Y. S. Huang

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)159-167
Number of pages9
JournalBiomedical Engineering - Applications, Basis and Communications
Volume13
Issue number4
DOIs
Publication statusPublished - 2001 Aug 25

Fingerprint

Engines
Liver
Computerized tomography
Tomography
Uncertainty
Cysts
Liver Diseases
Tumors
Hepatocellular Carcinoma
Theoretical Models
Textures
Learning
Tissue
Mathematical models
Neoplasms

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Bioengineering
  • Biomedical Engineering

Cite this

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title = "Using a fuzzy engine and complete set of features for hepatic diseases diagnosis: Integrating contrast and non-contrast CT images",
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.",
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Using a fuzzy engine and complete set of features for hepatic diseases diagnosis : Integrating contrast and non-contrast CT images. / Chen, E. L.; Chung, Yi-Nung; Chung, P. C.; Tsai, H. M.; Huang, Y. S.

In: Biomedical Engineering - Applications, Basis and Communications, Vol. 13, No. 4, 25.08.2001, p. 159-167.

Research output: Contribution to journalArticle

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T2 - Integrating contrast and non-contrast CT images

AU - Chen, E. L.

AU - Chung, Yi-Nung

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AU - Huang, Y. S.

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