Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model.
In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.
Khayati
Rasoul
R
Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Vafadust
Mansur
M
Towhidkhah
Farzad
F
Nabavi
Massood
M
D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000175:diagnosis*; D011336:Probability; D015203:Reproducibility of Results