Comparative Analysis of Liver Segmentation Using K-Means, Fuzzy C-Means and Spatial Fcm Using Mumford Shah Approach
Frizilin R1, Muthukumaravel A2

1Frizilin R, Research Scholar, Bharathiar University, Coimbatore (Tamil Nadu), India.
2Muthukumaravel A, Professor Head, Department of MCA, Bharath Institute of Higher Education and Research, Selaiyur, Chennai (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 07 May 2019 | PP: 99-104 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1020376S19/2019©BEIESP
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Abstract: In medical environment, liver segmentation plays an significant 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 technique is used to improve the robustness with less time consumption. This study has motivated 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.
Keywords: Liver Segmentation, K-Means Clustering (KMC), Fuzzy C-Means Clustering, Spatial Fuzzy Clustering (SFCM).
Scope of the Article: Fuzzy Logics