Medical Image Segmentation Based on Fuzzy Controlled Level Set and Local Statistical Constraints

Image segmentation is one of the most important fields in artificial vision due to its complexity and the diversity of its application to different image cases. In this paper, a new Region of Interest (ROI) segmentation in medical images approach is proposed, based on modified level sets controlled by fuzzy rules and incorporating local statistical constraints (mean, variance) in level set evolution function, and low image resolution analysis by estimating statistical constraints and curvature of curve at low image scale. The image and curve at low resolution provide information on rough variation of respectively image intensity and curvature value. The weights of different constraints are controlled and adapted by fuzzy rules which regularize their influence. The objective of using low resolution image analysis is to avoid stopping the evolution of the level set curve at local maxima or minima of images. This method is tested on medical images. The obtained results of the technique presented are satisfying and give a good precision.