Fuzzy C-Means for image segmentation: challenges and solutions

Image segmentation is considered a pertinent prerequisite for numerous tasks in digital image processing. The procedure through which identical segments in an image are identified is termed digital image segmentation and well-known clustering techniques are incorporated for the same. The Fuzzy C-Means algorithm, popularly known as FCM, is one of the most extensively employed clustering methodologies, but it has several drawbacks, including a costly computational time complexity, initial cluster centers, membership matrix reliance, and noise sensitivity. An up-to-date review of the solutions mentioned in recent literature to overcome the issues has been presented in this paper. Further, the main issues involved in the development of these improved FCM variants are deliberated.

Automated summary

Image segmentation is a crucial prerequisite for various tasks in digital image processing, and it involves identifying identical segments in an image using well-known clustering techniques. The Fuzzy C-Means algorithm (FCM), being extensively used, has drawbacks such as high computational time complexity, reliance on initial cluster centers and membership matrix, and sensitivity to noise. This paper presents a review of recent literature solutions to overcome these issues and discusses the main challenges in developing improved FCM variants.