Abstract
Fuzzy C-Means (FCM) algorithm is one of the most commonly used image segmentation algorithms. It has the advantages of unsupervised, easy calculation, soft segmentation and so on. However, for the image containing noise, it will be more obviously disturbed. At the same time, it is sensitive to the initial value and easy to fall into the local minimum. Aiming at solving above problems, a new FCM algorithm is proposed, which combines wavelet transform and improved FCM algorithm. Firstly, the high frequency and low frequency coefficients of different scales are obtained by using the wavelet transform to decompose the image. The Anisotropic Diffusion is used to denoise the decomposed high frequency coefficients. Then, the processed coefficients are reconstructed by wavelet to get the processed images. Finally, the particle swarm optimization algorithm is used to update the FCM cluster centers to get the global optimal value. The experimental results show that the proposed algorithm can better suppress the influence of noise and has better robustness.
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.
