Image Segmentation Based on Fuzzy C Means Clustering Algorithm and Morphological Reconstruction

The purpose of segmentation is to depict an original picture in something easier to interpret. Generally, in image processing watershed algorithm is used essentially for segmentation purposes which is fast and simple method and requires low computation time. But, it has disadvantages causing excessive segmentation and this method is sensitive of falsifying edges. The fuzzy c means (FCM) technique is extremely successful when segmenting images. Fuzzy c means clustering's biggest advantage is the high identification rate and the lower false location rate. Nevertheless, the fuzzy c means algorithm is noise-sensitive. To overcome these problems, an improved image segmentation algorithm based on morphological reconstruction and fuzzy c means algorithm is presented in order to improve the performance of the segmentation. Firstly, principle component analysis method is applied to reduce number of variables in data by extracting important one from large pool. Secondly, morphological reconstruction operation is introduced which guarantees the immunity to noise. Thirdly, fuzzy c means algorithm is applied. Finally, digital images are segmented by using this proposed method. Segmented findings indicate that better segmentation efficiency than watershed algorithm and fuzzy c means algorithm were obtained with proposed approach.