Title :
Segmentation of MR image based on maximum a posterior
Author :
Liu, F. ; Gao, S. ; Gao, X.
Author_Institution :
Dept. of Electr. Eng., Tsinghua Univ., Beijing, China
Abstract :
Brain MR image segmentation takes an important role in research and clinical application. Statistical method is effective in the segmentation, which is usually based on maximum a posterior (MAP). The key of MAP method is to estimate a prior probability of the segmentation. Multilevel logistic (MILL) model has been used in practice for the estimation. To further improve the performance of the segmentation, a weighted MLL (WMLL) model is proposed in this paper. The simulated results show that the WMILL model is effective.
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; a prior probability; brain MR image segmentation; magnetic resonance imaging; maximum a posterior; medical diagnostic imaging; simulated results; statistical method; weghted multilevel logistic model; Biological neural networks; Biological tissues; Biomedical engineering; Brain modeling; Genetic algorithms; Image segmentation; Logistics; Probability; Random variables; Statistical analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1017335
