ABSTRACT
Segmentation is a key step towards image analysis in various image processing applications such as object recognition, pattern recognition and medical imaging. It can be defined as a grouping in a parameter space where points are associated with different sets of values of similar intensities in different images. So, grouping is the main step of image segmentation. This type of segmentation is called clustering which is very important in classifying different patterns/structures in an image. Clustering is a technique to separate unlabelled data into finite and discrete sets. It can be done using the fuzzy or non-fuzzy method. Traditional non-fuzzy clustering like k-means puts data into exactly one cluster. But for overlapped data sets where some data may be allocated to multiple clusters, k-means clustering may not analyse the data set clearly. To achieve better clustering, fuzzy c means clustering is used. The first fuzzy method to segment the regions of an image is the fuzzy c means (FCM) clustering, introduced by Bezdek et al. [2]. Clustering may be hard c means or FCM. Hard c means is a non-fuzzy method, which is also known as k means clustering. K means clustering partitions a collection of N vectors into k groups. It executes a sharp classification in which each object is assigned to a class or not. Also, there is very often no sharp boundary between clusters in many real-time images. This problem can be alleviated by associating a membership value in the interval [0, 1] to data in every cluster such that data that have a similarity with each cluster with membership values near 0 signify a small similarity between the sample and the cluster and data with membership values near 1 signify a high degree of similarity. Medical images contain a lot of uncertainties, and there are hardly sharp boundaries present and so fuzzy clustering may be very much beneficial. FCM partitions the data in such a way that a data point can belong to all groups in different membership grades where an element may have partial membership grades in several clusters – herein lies the distinction
between the hard and fuzzy c partitions of data set X. It is an iterative algorithm where the aim is to find the cluster centres that minimize the dissimilarity function. This is an important feature in medical image diagnosis to increase the sensitivity.
