Medical Image Segmentation Based on Fuzzy Controlled Level
Set and Local Statistical Constraints
Mohamed Benzian1,2 and Nacéra Benamrane2
1Département d'Informatique, Université
Abou Bekr Belkaid-Tlemcen, Algérie
2Laboratoire SIMPA, Département
d'Informatique, Université des Sciences et de la Technologie d'Oran Mohamed
Boudiaf, Algérie
Abstract: 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.
Keywords: Segmentation, level
sets, medical images, image resolution, fuzzy rules, ROI.
Received April 8, 2015; accepted December 23, 2015