Fuzzy clustering based transition region extraction for image segmentation

Abstract

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed clustering approach based transition region extraction method for image segmentation. The proposed method initially uses the local variance of the input image to get the variance feature image. Fuzzy C-means clustering is applied to the variance feature image to separate the transitional features from the feature image. Further, Otsu thresholding is applied to the transitional feature image to extract the transition region. For extracting the exact edge image, morphological thinning operation is performed. The edge image extracted in former step is closed in nature. The morphological cleaning and region filling operation is performed on an edge image to get the object regions. Finally, objects are extracted via these object regions. The proposed method is compared with different image segmentation methods. An experimental result reveals that the proposed method outperforms other methods for segmentation of images containing single and multiple objects.