title: IFCM Based Segmentation Method for Liver Ultrasound Images.
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.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India. nishantjain86@gmail.com.
authors.forename: Nishant
authors.identifier: http://orcid.org/0000-0002-4380-6325
authors.initials: N
authors.lastname: Jain
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India.
authors.forename: Vinod
authors.initials: V
authors.lastname: Kumar
mesh_terms: D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000379:methods*
keywords: Active contour method; Fuzzy C-mean; Image processing; Image segmentation; Liver; Ultrasound imaging