CT image segmentation based on automatic adaptive minimal fuzzy entropy measure.
- Author:
Guifang GONG
1
;
Chengde FENG
;
Hui ZHANG
;
Yanfang ZHU
Author Information
1. College of Manufacturing Science and Engineer, Sichuan University, Chengdu 610065, China. gongguifang0805@163.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
diagnostic imaging;
Entropy;
Fuzzy Logic;
Humans;
Image Processing, Computer-Assisted;
Radiographic Image Enhancement;
Radiographic Image Interpretation, Computer-Assisted;
methods;
Tomography, X-Ray Computed;
methods
- From:
Journal of Biomedical Engineering
2008;25(2):304-308
- CountryChina
- Language:Chinese
-
Abstract:
In order to extract the anatomical feature of several tissues from CT image and solve the contradiction between the improvement of searching speed and the instability of results,we propose a method for image segmentation using auto adaptive minimal fuzzy entropy measure. Firstly, to find the optimal threshoding for segmenting image, the values of the exponent parameters of membership function of fuzzy subsets and the range of the searching thresholding values can be determined by using the iterative approach and the image histogram, and then the thresholding of minimizing the fuzzy entropy is implemented by searching all possible combinations of every thresholding in determinate searching range. The experiment results show that our proposed method facilitates good performance for CT image segmentation. The searching speed is quick, the segmented images show more details, and the results of many runs are steadier than those obtained by using genetic algorithm or simulated annealing algorithm.
