CT image segmentation based on automatic adaptive minimal fuzzy entropy measure / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 304-308, 2008.
Article
Dans Chinois
| WPRIM
| ID: wpr-291244
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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Algorithmes
/
Traitement d'image par ordinateur
/
Encéphale
/
Imagerie diagnostique
/
Interprétation d'images radiographiques assistée par ordinateur
/
Amélioration d'image radiographique
/
Tomodensitométrie
/
Logique floue
/
Entropie
/
Méthodes
Type d'étude:
Etude diagnostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2008
Type:
Article
Documents relatifs à ce sujet
MEDLINE
...
LILACS
LIS