COMPARITIVE ANALYSIS OF K++GKFCM CLUSTERING FOR BRAIN TUMOUR DETECTION

Abstract

Due to the complicated structure of brain tumors, hazy borders, and outside variables like noise, inferring tumor and edema regions from brain magnetic resonance imaging (MRI) data is still difficult. In this paper, a powerful hybrid clustering technique together with morphological procedures is suggested for segmenting brain tumors in order to reduce noise sensitivity and enhance segmentation stability. The following are the paper's key contributions: initially, adaptive Wiener filtering is employed for de-noising, and morphological procedures are applied for deleting non-brain tissue, thereby minimizing the method’s susceptibility to noise. Second, to segment pictures, the fuzzy C-means technique based on a Gaussian kernel is used with K-means++ clustering. This clustering decreases the sensitivity of the clustering parameters while simultaneously enhancing the stability of the algorithm. To produce accurate representations of brain tumours, the retrieved tumour pictures are lastly post processed utilising morphological procedures and median filtering. The suggested approach was also contrasted with other existing segmentation algorithms.