Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

Abstract

Accurate liver segmentation from computed tomography (CT) scan images is an essential and crucial step in computer-aided diagnosis of liver tumor. The automatic and unsupervised methods are used to segment CT image that reduce the time and eliminate the need for manual interface. In this paper, the adaptive threshold, morphological processing, and kernel fuzzy C-means (KFCM) clustering algorithm have been used with spatial information for visualizing and measuring the tumor area of liver which is segmented from CT abdominal images. The proposed process was tested and evaluated to a series of test images on MICCAI 2008 liver tumor segmentation challenge datasets to extract the tumor region. The area of the tumor reason has been calculated also. The KFCM algorithm introduces a kernel function on fuzzy c-means clustering (FCM) to reduce the effect of noise and improves the ability of clustering. Experimental results showed positive results for the proposed algorithm. The PSNR and MSE have been calculated for FCM and KFCM method to compare the results to segment the images. An experimental result shows that the proposed method could be used effectively to segment the liver cancer region in terms of high PSNR and low MSE values and achieves high consistency to detect small changes in images.