Fuzzy Clustering Algorithms for Effective Medical Image Segmentation

Abstract

Abstract-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.

A New Approach for MR Brain Image Segmentation using Intuitionistic Fuzzy Complement

“…This paper provides a new approach for IFCM algorithm by using Intuitionistic fuzzy complement [6].From the experimental results it can be seen that the performance of proposed method has better segmentation according to the segmentation results and the values of converging rate and computational time as compared to the existing methods [3,5] for the considered MR brain image.In future,the proposed method is experimenting on other medical images.…”

“…In [5]Deepali Aneja and Tarun Kumar Rawat used Yager IFG to construct intuitionistic fuzzy image.Yager's intuitionistic fuzzy complement is,…”

“…Motivated from Zadeh's definition of fuzzy sets theory [9],where uncertainty or vagueness is considered only in the form of membership function.Higher order fuzzy sets have been presented by various researchers,among them Intuitionistic fuzzy sets proposed by Atanassov [1] has been a suitable tool for modelling the hesitancy arising from imprecise or/and imperfect information.This hesitation is due to the lack of knowledge or the personal error in defining the membership function.Intuitionistic fuzzy sets are defined by two characteristic functions,namely the membership and the non membership,describing the belongingness and non belongingness of an element respectively.Image segmentation [3] is one of the major step in medical image processing.Imaging can be used to visualize different anatomical parts of human body from X-ray,ultrasound,MRI etc.Brain imaging is useful in detection of brain tumor,stroke,paralysis etc.Segmentation methods that includes the classification of tissues in medical imagery can be performed using a variety of techniques.Many clustering strategies have been used such as the crisp clustering scheme and the fuzzy clustering scheme [6].The fuzzy set theory [6] which involves the idea of partial membership described by a membership function,fuzzy clustering as a soft segmentation method has been widely studied and successfully applied to image segmentation.Although the conventional FCM algorithm has a serious limitation.Chaira [4] introduced intuitionistic fuzzy entropy in objective function of conventional clustering algorithm and applied to medical images.Deepali Aneja and Tarun Kumar Rawat [5]used Yager's complement [6]to calculate hesitation factor in IFCM algorithm for the effective medical image segmentation.…”

Robust FCM Algorithm with Local and Gray Information for Image Segmentation

“…Clustering unlabeled data into the most homogeneous groups is a problem that has received extensive attention in many application domains [1][2][3]. Thus, several clustering methods have been developed.…”

“…The hard (or crisp), probabilistic, and possibilistic c-means [4] are the well-known partitioning methods that have been extended to many different versions based on the data type and the application purpose. The probabilistic or fuzzy c-means (FCM) is always used to generate fuzzy partitions and, thus, it is widely useful to segment images [2,5] where the fuzzy data is redundant. In fact, Abdel-Maksoud et al [6] used the fuzzy c-means algorithm combined with its hard version k-means to extract brain tumors from MRI (Magnetic Resonance Imaging) images.…”