A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images.
It is very important to detect stages of multiple sclerosis (MS) lesions in order to exactly quantify involved voxels. In this paper, a novel method is proposed for automatic detection of different stages of MS lesions in the brain magnetic resonance (MR) images, in fluid attenuated inversion recovery (FLAIR) studies. In the proposed method, firstly, MS lesion voxels are segmented in FLAIR images based on adaptive mixtures method (AMM) and Markov Random Field (MRF) model. Then, signal intensity of each lesion voxel is modeled as a linear combination of signals related to the normal and also abnormal parts, in the voxel. By applying an optimal threshold, voxels with new intensities are primarily classified into two stages: previously destructed (chronic) and on going destruction (acute) lesions. Finally, the acute lesions, according to their activities, are classified, by another optimal threshold, into two new stages, early and recent acute. Evaluation of the proposed method was performed by manual segmentation of chronic and enhanced (early) acute lesions in gadolinium enhanced T1-weighted (Gad-E-T1-w) images by studying T1-weighted (T1-w) and T2-weighted (T2-w) images, using similarity criteria. The results showed a good correlation between the lesions segmented by the proposed method and by experts manually. Thus, the suggested method is useful to reduce the need for paramagnetic materials in contrast enhanced MR imaging which is a routine procedure for separation of acute and chronic lesions.
Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Rasoul
R
Khayati
Mansur
M
Vafadust
Farzad
F
Towhidkhah
S Massood
SM
Nabavi
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D018450:Disease Progression; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D009103:Multiple Sclerosis / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity