Title :
Local Hybrid Level-set Method for MRA Image Segmentation
Author :
Hong, Qingqi ; Li, Qingde ; Tian, Jie
Author_Institution :
Dept. of Comput. Sci., Univ. of Hull, Kingston upon Hull, UK
fDate :
June 29 2010-July 1 2010
Abstract :
In this paper, a local hybrid level-set method for medical image segmentation is presented. In proposed method, a locally fitted binary energy function is introduced into the hybrid level-set framework proposed by Zhang et al.. Compared with the globally specified threshold, the use of local binary fitting energy in the hybrid level-set method allows one to extract local image information more accurately, which is essential for enhancing the efficiency and effectiveness of the segmentation of inhomogeneous images. Experimental results on 3D medical images are presented to demonstrate the strengths of the proposed method.
Keywords :
feature extraction; image segmentation; medical image processing; 3D medical image; MRA image segmentation; binary energy function; inhomogeneous image; local hybrid level set method; local image information extraction; Active contours; Biomedical imaging; Fitting; Image segmentation; Mathematical model; Nonhomogeneous media; Three dimensional displays; Intensity inhomogeneity; LBF energy; Level set; MRA image;
Conference_Titel :
Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on
Conference_Location :
Bradford
Print_ISBN :
978-1-4244-7547-6
DOI :
10.1109/CIT.2010.250