Fuzzy Clustering Algorithms for Effective Medical Image Segmentation
Deepali Aneja, Tarun Kumar Rawat · International Journal of Intelligent Systems and Applications · 2013
Medical image segmentation demands a segmentation algorith m which works against noise.The most popular algorith m used in image segmentation is Fuzzy C-Means clustering.It uses only intensity values for clustering wh ich makes it highly sensitive to noise.The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM ), Intuitionistic Fuzzy C-Means (IFCM ), and Type-II Fu zzy C-Means (T2FCM ) is presented in this paper.These algorith ms are executed in two scenarios-both in the absence and in the presence of noise and on two kinds of images -Bacteria and CT scan brain image.In the bacteria image, clustering differentiates the bacteria fro m the background and in the brain CT scan image, clustering is used to identify the abnormality region.Perfo rmance is analyzed on the basis cluster validity functions, execution time and convergence rate.Misclassification error is also calculated for brain image analysis.