Fundamentals of speech recognition
Fundamentals of speech recognition
Segmentation of brain tumors in 4D MR images using the hidden Markov model
Computer Methods and Programs in Biomedicine
A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Information Technology in Biomedicine
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods
Computer Methods and Programs in Biomedicine
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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.