title: Segmentation of brain tissues using a 3-D multi-layer hidden Markov model.
abstract: To compensate for bias field inhomogeneity and reduce noise, we incorporate domain-based knowledge and spatial information into a brain segmentation algorithm by proposing a new multi-layer Hidden Markov model. Brain tissues include Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). A typical slice of a brain image either contains GM, GM-WM or GM-WM-CSF. Therefore, we classify the slices into three classes by employing a 1-D Hidden Markov model in the first layer of our method. Corresponding to a class in the first layer, we use another 1-D Hidden Markov model for segmentation of the slices in the second layer. A 2-D slice is converted into a vector by concatenation of the individual rows. Then, it is segmented by a second layer model. We extensively evaluated our method using three public datasets including 5492 images. Our method proves the significant potential of the proposed multi-layer Hidden Markov model for segmentation of 3-D medical image in the presence of noise and field inhomogeneity. Regarding the IBSR_18 datasets, the proposed method improved the results of segmentation of White Matter and Gray Matter by 0.026 and 0.04, respectively, using Dice coefficient index.
authors.affiliation: Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran. aforuzan@yahoo.com
authors.forename: Amir H
authors.initials: AH
authors.lastname: Foruzan
authors.forename: Iman
authors.initials: I
authors.lastname: Kalantari Khandani
authors.forename: Shahriar
authors.initials: S
authors.lastname: Baradaran Shokouhi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D002648:Child; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008959:Models, Neurological