A Comparative Study on CT Image Segmentation Using FCM-based Clustering Methods
نویسندگان
چکیده
Identifying specific CT-image regions is an important process in medical diagnosis. Clustering is a simple and useful means for automatic image segmentation. However, clustering results vary with the features of image pixels and the settings of parameters of the clustering methods. This study compares the results of CT image segmentation using FCMbased clustering algorithms running with intensityand texturebased image features. Three types of image features, grayscale, LBP, and grayscale+LBP, are investigated. KM, FCM, and their medoid-variations are tested with various parameter settings. The results show that FCM and the grayscale+LBP feature can produce reasonable and satisfactory clustering results for CT-image segmentation.
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