Image clustering using improved spatial fuzzy C-means

Abstract

Image segmentation plays vital role in computer vision, pattern recognition, disease diagnosing in medical images and many other fields. Medical image segmentation is one of the most attractive areas of research, because successfulness of the post-processing techniques highly depends on the accuracy of image segmentation. Further, during acquisition medical images may have been corrupted from various types of degradations and noises. In medical imagery, ultrasound images are usually of poor quality due to the presence of speckle noise and wave interferences. It takes considerable efforts from experts to analyze and make decision on the basis of acquired images. In this paper, we propose an approach called improved spatial fuzzy c-means modified (sFCMM) for image segmentation. This technique works well compared to conventional fuzzy c-means (FCM) and spatial fuzzy c-means (sFCM) algorithms. Three types of features: moments of gray level histogram (MGH), 2D continuous wavelet transforms (2D-CWT) and gray level co-occurrence matrix (GLCM), are extracted from the ultrasound images for segmentation. Clustering validity indices are computed and have been compared with FCM and sFCM algorithm. Experimental results show the effectiveness of the proposed technique.