Cited By
View all- Kutbay U(2020)Automated hemangioma detection using Otsu based binarized Kaze featuresMultimedia Tools and Applications10.1007/s11042-020-09156-279:33-34(24781-24793)Online publication date: 25-Jun-2020
IFCM Based Segmentation Method for Liver Ultrasound Images
Authors: 
Nishant Jain, Vinod KumarAuthors Info & Claims
Pages 1 - 12
Abstract
In this paper we have proposed an iterative Fuzzy C-Mean (IFCM) method which divides the pixels present in the image into a set of clusters. This set of clusters is then used to segment a focal liver lesion from a liver ultrasound image. Advantage of IFCM methods is that n-clusters FCM method may lead to non-uniform distribution of centroids, whereas in IFCM method centroids will always be uniformly distributed. Proposed method is compared with the edge based Active contour Chan-Vese (CV) method, and MAP-MRF method by implementing the methods on MATLAB. Proposed method is also compared with region based active contour region-scalable fitting energy (RSFE) method whose MATLAB code is available in author's website. Since no comparison is available on a common database, the performance of three methods and the proposed method have been compared on liver ultrasound (US) images available with us. Proposed method gives the best accuracy of 99.8 % as compared to accuracy of 99.46 %, 95.81 % and 90.08 % given by CV, MAP-MRF and RSFE methods respectively. Computation time taken by the proposed segmentation method for segmentation is 14.25 s as compared to 44.71, 41.27 and 49.02 s taken by CV, MAP-MRF and RSFE methods respectively.
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- IFCM Based Segmentation Method for Liver Ultrasound Images
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Published: 01 November 2016
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View all- Kutbay U(2020)Automated hemangioma detection using Otsu based binarized Kaze featuresMultimedia Tools and Applications10.1007/s11042-020-09156-279:33-34(24781-24793)Online publication date: 25-Jun-2020