Comparative Analysis of Liver Segmentation Using K-Means, Fuzzy c-Means and Spatial FCM Using Mumford Shah Approach

Abstract

In medical environment, liver segmentation plays an important role by helping physicians to find the location of affected
region of the liver from the normal part. Automatic extraction which is recognized as segmentation by computer processing
may diminish the burden as well as time consuming. To overcome the burden of identification of definite organ
segmentation technique is used. In general liver analysis have done through CT scan or MRI scan whereas the image of
MRI scan is utilized for the complete process which is more convenient than other scanning while diagnosing. This does
not create harmful to human body due to no practice of radiation. As the initial stage, image segmentation has been done
through spatial fuzzy clustering which can control level of its parameter to set advancement as a result in the fuzzy
clustering. In this advanced technique, mumford shah method is used to improve the robustness with less time
consumption. This study has focused on both less time extraction and also the accuracy. So, in order to attain spatial fuzzy
clustering is proposed and also compared with the existing clustering algorithm such as K-means, Fuzzy C-Mean (FCM) to
evaluate the performance efficiency in time extraction using clustering outcomes.