Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model

Rasoul Khayati; Mansur Vafadust; Farzad Towhidkhah; Massood Nabavi

doi:10.1016/j.compbiomed.2007.12.005

Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model

Comput Biol Med. 2008 Mar;38(3):379-90. doi: 10.1016/j.compbiomed.2007.12.005. Epub 2008 Feb 11.

Authors

Rasoul Khayati¹, Mansur Vafadust, Farzad Towhidkhah, Massood Nabavi

Affiliation

¹ Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.

PMID: 18262511
DOI: 10.1016/j.compbiomed.2007.12.005

Abstract

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.

MeSH terms

Adult
Algorithms
Bayes Theorem
False Negative Reactions
False Positive Reactions
Female
Humans
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Male
Markov Chains
Models, Statistical*
Multiple Sclerosis / diagnosis*
Probability
Reproducibility of Results