DocumentCode :
2519473
Title :
Segmentation of Brain MR Images Based on T-Mixture Model
Author :
Zhao, Haifeng ; Xu, Xingming ; Chen, Sibao ; Luo, Bin
Author_Institution :
Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China
fYear :
2009
fDate :
11-13 June 2009
Firstpage :
1
Lastpage :
4
Abstract :
As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer- aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation.
Keywords :
Bayes methods; biomedical MRI; brain; image segmentation; medical image processing; Bayes decision rule; brain MR images; computer-aided diagnosis; magnetic resonance imaging; medical image segmentation; posterior probability; radiological evaluation; t-mixture model; white matter segmentation; Anatomical structure; Biomedical imaging; Brain modeling; Educational technology; Hidden Markov models; Image segmentation; Magnetic resonance imaging; Mathematical model; Medical diagnostic imaging; Surgery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
Type :
conf
DOI :
10.1109/ICBBE.2009.5163370
Filename :
5163370
Link To Document :
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