This paper addresses the challenge of accurately segmenting complex regions in medical images, where traditional clustering methods often struggle due to noise sensitivity and unclear boundaries. Our objective is to develop a robust clustering approach for medical image segmentation. We present Kernelized Type 2 Intuitionistic Fuzzy C Means (KT2IFCM), which integrates a Radial Basis Function (RBF) kernel with Type 2 Intuitionistic Membership and hesitation degree. This method improves boundary definition, centroid placement, and handles non-linear structures. Results from tests on 14 datasets (4 simulated, 10 real brain MRI scans) show that KT2IFCM achieves superior noise resilience and segmentation accuracy compared to FCM, IFCM, KIFCM, and T2IFCM. The novelty lies in combining kernel mapping with intuitionistic fuzzy membership to deliver more reliable segmentation in medical imaging. Statistical analysis using the Friedman test further confirms KT2IFCM's improved accuracy over competing methods on synthetic datasets.
Bhalla et al. (Wed,) studied this question.