A novel brain image segmentation using intuitionistic fuzzy C means algorithm. Issue 1 (March 2016)

• Record Type: Journal Article
• Authors: Prabu, C.; Bavithiraja, S.V.M.G.; Narayanamoorthy, S.
• Is Part Of: International journal of imaging systems and technology. Volume 26:Issue 1(2016:Mar.)
• Page Start: 24
• Page End: 28
• Publication Date: 2016-03
• Subjects: image segmentation -- fuzzy C means -- intuitionistic fuzzy set -- membership function; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22153
• Languages: English
• ISSNs: 0899-9457

Abstract

ABSTRACT: A process of splitting the image into pixel bands is the image segmentation. As medical imaging contain uncertainties, there are difficulties in classification of images into homogeneous regions. There is a need for segmentation algorithm for removing the noise from the medical image segmentation. The very popular algorithm is Fuzzy C‐Means (FCM) algorithm used for image segmentation. Fuzzy sets, rough sets, and the combination of fuzzy and rough sets play a prominent role in formalizing uncertainty, vagueness, and incompleteness in diagnosis. But it will use intensity values only which will be highly sensitive to noise. In this article, an Intuitionistic FCM (IFCM) algorithm is presented for clustering. Intuitionistic fuzzy (IF) sets are generalized sets and their elements are characterized by a membership value as well as nonmembership value. This IFCM has an uncertainty parameter which is called hesitation degree and a new objective function is integrated in the standard FCM based on IF entropy. The IFCM will provide better performance than FCM for image segmentation.