SEGMENTATION OF IMAGES USING HISTOGRAM BASED FCM CLUSTERING ALGORITHM AND SPATIAL PROBABILITY

During last decades, image segmentation has been a interestinging area for research and developing efficient algorithms. Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The renowned conventional fuzzy c-means algorithm is efficiently used for clustering in medical image segmentation. But FCM is highly sensitive to noise because it uses only intensity values for clustering. So in this paper for the segmentation, histogram based efficient fuzzy c-means algorithm is proposed. The input image is clustered using proposed Improved Histogram based Spatial FCM algorithm. Robustness against noise is improved by using the spatial probability of the neighboring pixel. The medical images are denoised, before to segmentation with effective denoising algorithm. Comparative study has been done between conventional FCM and proposed method. The histogram based experimental results has obtained and shows that the proposed approach gives reliable segmentation accuracy with noise levels. And it is clear that the proposed approach is more efficient compared to conventional FCM.