A Comparative Study of Color Image Segmentation Using Hard, Fuzzy,Rough Set Based Clustering Techniques

Abstract

Image segmentation is the process of subdividing an image into its constituent parts and extracting these parts of interest, which are the objects. Colour image segmentation emerges as a new area of research. It can solve many contemporary problems in medical imaging, mining and mineral imaging, bioinformatics, and material sciences. Naturally, color image segmentation demands well defined borders of different objects in an image. So, there is a fundamental demand of accuracy. The segmented regions or components should not be further away from the true object than one or a few pixels. So, there is a need for improved image segmentation technique that can segment different components precisely. Image data may have corrupted values due to the usual limitations or artifacts of imaging devices. Noisy data, data sparsity, and high dimensionality of data create difficulties in image pixel clustering. As a result, image pixel clustering becomes a harder problem than other form of data.

 Taking into account all the above considerations we propose an unsupervised image segmentation method using Rough-Fuzzy C-Mean a hybrid model for segmenting RGB image by reducing cluster centers using rough sets and Fuzzy C-Means Method, and also compare the effectiveness of the clustering methods such as Hard C Means (HCM), Fuzzy C Means (FCM), Fuzzy K Means (FKM), Rough C Means (RCM) with cluster validity index such as DB Index, XB Index and Dunn Index. A good clustering procedure should make the value of DB index as low as possible, for Dunn Index high value, and for XB Index low value.