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Proceedings Paper

Unsupervised classification of cirrhotic livers using MRI data
Author(s): Gobert Lee; Masayuki Kanematsu; Hiroki Kato; Hiroshi Kondo; Xiangrong Zhou; Takeshi Hara; Hiroshi Fujita; Hiroaki Hoshi
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Paper Abstract

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691514 (17 March 2008); doi: 10.1117/12.770862
Show Author Affiliations
Gobert Lee, Gifu Univ. (Japan)
Masayuki Kanematsu, Gifu Univ. School of Medicine (Japan)
Hiroki Kato, Gifu Univ. School of Medicine (Japan)
Hiroshi Kondo, Gifu Univ. School of Medicine (Japan)
Xiangrong Zhou, Gifu Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)
Hiroaki Hoshi, Gifu Univ. School of Medicine (Japan)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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