Segmentation of MS lesions using entropy-based EM algorithm and Markov random fields

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

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

“…Their technique is related to the idea that FLAIR images should be more important than T1w images in the classification of WML, although T1w images provide a better distinction between the GM and WM classes. Bijar et al (Bijar et al, 2011) proposed a monomodal EM-approach using the entropy instead of the likelihood as cost function using only FLAIR images for the segmentation.…”

Increasing the Contrast of the Brain MR FLAIR Images Using Fuzzy Membership Functions and Structural Similarity Indices in Order to Segment MS Lesions

“…The success of a template-based segmentation algorithm depends on the outcome of the template. Bijar et al [39] presented an automatic segmentation of MS lesions in FLAIR MR images. The proposed method estimated a gaussian mixture model with three kernels as CSF, normal tissue and MS lesions.…”

“…Anbeek et al [24] and Admiraal-Behloul et al [48] made use of FLAIR images for the segmentation of white matter lesions in patients of (Mean SD: 65.67.7) years old. In comparison, we used FLAIR images for the segmentation of MS lesions in younger patients (Mean SD: 298), which were also used by Khayati et al [38] and Bijar et al [39]. For the patients with small lesion load, Anbeek et al [24], Admiraal-Behloul et al [48], Khayati et al [38] and Bijar et al [39] reached values of 0.5, 0.7, 0.7253 and 0.7262 for SI, respectively, while we obtained a value of 0.7261 for SI, according to Table 4.…”

“…In comparison, we used FLAIR images for the segmentation of MS lesions in younger patients (Mean SD: 298), which were also used by Khayati et al [38] and Bijar et al [39]. For the patients with small lesion load, Anbeek et al [24], Admiraal-Behloul et al [48], Khayati et al [38] and Bijar et al [39] reached values of 0.5, 0.7, 0.7253 and 0.7262 for SI, respectively, while we obtained a value of 0.7261 for SI, according to Table 4. As seen in Table 4, our proposed method improves the value of SI by about 2% for the patients with a moderate lesion load in comparison with others.…”

“…The methods in this second group employ the image intensities previously segmented by an expert, to train a classifier which segments the tissues and lesions of the MR images. Unsupervised strategies, where no prior knowledge is used, have two different subgroups: a sub-group of methods that segments brain tissue to help lesion segmentation [33]–[39]; and another sub-group that only uses the lesion properties for segmentation [40]–[44]. In the first sub-group, methods consist of either segmenting the tissue first and then the MS lesions, or segmenting the tissue and the lesions at the same time.…”