A Novel Image Segmentation Algorithm Based on Fuzzy C-means Algorithm and Neutrosophic Set

Image segment is an important step in image processing, pattern recognition and computer vision. Numerous algorithms have been proposed to in this field for last twenty years. However, a generalized segmentation method, especial for noisy image, are not studied greatly. A neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view for a given application or field. We apply the neutrosophic set in image domain and define some concepts and operations for image segmentation. The image G is transformed into NS domain. Then, the entropy in neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, mean operation is proposed to reduce the set indeterminancy. Finally, a new fuzzy c-means algorithm, α-fuzzy-c-means (αFCM) is proposed to segment the image on NS domain. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment the images automatically and effectively. Especially, it can process the “clean” images and the images with noise without knowing its type, which is the most difficult task for image segmentation.