State-of-The-Art Fuzzy Active Contour Models for Image Segmentation

TL;DR

This paper reviews and experimentally evaluates various fuzzy energy based active contour models for image segmentation, analyzing their strengths and weaknesses across diverse image conditions.

Contribution

It provides a comprehensive theoretical review and large-scale experimental comparison of fuzzy active contour models for improved understanding.

Findings

Different models show varying robustness to noise and blur.

Some models outperform others in low contrast and inhomogeneous regions.

The paper identifies key challenges and future directions in fuzzy active contour segmentation.

Abstract

Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic.