Abstract
Due to the insufficient use of local information, the traditional fuzzy C-means (FCM) algorithm and its extension algorithm combined with spatial information show poor robustness and low segmentation accuracy. In addition, in the process of image segmentation based on the FCM algorithm, the initial center estimation is regarded as the process of searching the appropriate value in the gray range. To solve these problems, a new robust algorithm is proposed in this paper. The algorithm searches the optimal initial center by introducing an improved parallel Lévy grey wolf optimization algorithm, which is an improved fuzzy C-means segmentation algorithm that combines local information and adaptive gray weighting. Experimental results infer that both the precision and efficiency of the proposed method are superior to those of the state-of-arts.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
