title: Hybrid softcomputing model for lesion identification and information combination: some case studies.
abstract: Authors present segmentation and information combination of section of human brain images. Improved hybrid algorithm is presented for clustering, which integrates the concept of Rough sets, Fuzzy sets incorporated with probabilistic as well as possibilistic memberships. The segmented images are fused using wavelet and curvelet based techniques. Lower and upper approximations of Rough sets handle uncertainty, vagueness, and incompleteness in class definition. To accelerate the segmentation process, the RFPCM has been equipped with membership suppression mechanism, which creates competition among clusters to speed-up the clustering process using MR T1 and MR T2 images of section of human brain.
authors.affiliation: Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Madhya Pradesh Bhopal 462051, India. arpit.nitbpl@gmail.com
authors.forename: Arpit
authors.initials: A
authors.lastname: Srivastava
authors.forename: Abhinav
authors.initials: A
authors.lastname: Asati
authors.forename: Sandeep
authors.initials: S
authors.lastname: Kumar
authors.forename: Yash
authors.initials: Y
authors.lastname: Sharma
authors.forename: Mahua
authors.initials: M
authors.lastname: Bhattacharya
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods