Abstract
This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, Markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.
Highlights
Magnetic Resonance Imaging (MRI) permits the noninvasive detailed visualization of internal anatomical structures
This paper presents an approach for fully automatic segmentation of Multiple Sclerosis (MS) lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images
Our method can be divided into two main sections: at first, intensities of brain pixels are modeled using a gaussian mixture model which consists of three kernels as cerebrospinal fluid (CSF), normal tissue and MS lesions classes
Summary
Magnetic Resonance Imaging (MRI) permits the noninvasive detailed visualization of internal anatomical structures. Our method can be divided into two main sections: at first, intensities of brain pixels are modeled using a gaussian mixture model which consists of three kernels as CSF, normal tissue and MS lesions classes. This step starts with only one kernel and uses an entropy based EM algorithm to estimate three kernels as three mentioned classes in an automatic manner which does not need initial values for parameter estimation.
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