K-means Clustering for Classifying Unlabelled MRI Data

Texture analysis of the liver for the diagnosis of cirrhosis is usually region-of-interest (ROI) based. Integrity of the label of ROI data may be a problem due to sampling. This paper investigates the use of Kmeans clustering, an unsupervised classifier which does not depend on the label of the data, for classification. Moreover, a procedure for generating a ROC curve for k-means clustering is also described in this paper. Using a MRI database of 44 patients with 16 cirrhotic and 28 non-cirrhotic liver cases, k-means clustering achieves an area under the ROC curve (AUC) index of 0.704. This is comparable to the performance of a linear discriminant analysis (LDA) and an artificial neural network (ANN) with the former attains a resubstitution and an average leave-onecase-out AUC of 0.781 and 0.779, respectively, and the

• Type: Conference
• Year: 2007
• Where: DICTA
• Authors: Gobert N. Lee, Hiroshi Fujita
• Keywords: Applied Computing, Average Leave-onecase-out Auc, DICTA 2007, K-means Clustering, ROC Curve