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30 March 2007 Classification of cirrhotic liver in Gadolinium-enhanced MR images
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Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns. Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regions-of-interest (ROIs). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers. Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice. This is because the 'true disease states' of the ROIs are required for the training of the classifier but is, generally, not available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROIs in that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic livers.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gobert Lee, Yoshikazu Uchiyama, Xuejun Zhang, Masayuki Kanematsu, Xiangrong Zhou, Takeshi Hara, Hiroki Kato, Hiroshi Kondo, Hiroshi Fujita, and Hiroaki Hoshi "Classification of cirrhotic liver in Gadolinium-enhanced MR images", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651430 (30 March 2007);

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