Automatic segmentation of multiple sclerosis lesions in magnetic resonance images remains a challenging task. In this study, we present a fully automatic method to extract lesions from multi-sequence MRI (T1, T2, T2 FLAIR, Proton Density) within an EM based probabilistic framework. The method uses the available MRI sequences in a hierarchical, orderly manner. First the T2 FLAIR sequence is used to generate a segmentation of supra-tentorial lesions. Then T2 and T1 lesion loads are computed, providing an insight into lesion structure. A priori anatomical knowledge is incorporated in the form of a probabilistic brain atlas.
Background:The Watershed Transform consists of an image partitioning into its constitutive regions. This transform is easily adapted to be used in different types of images and it allows distinguishing complex objects. However, the implementation of the Watershed Transform for very complex images actually produces over-segmentation. In this paper we propose two algorithms to solve this over-segmentation problem.
Methods:We define internal markers, by algorithms based on clustering and fuzzy logic in order to join the oversegmented regions with statistical features. To define the algorithm parameters and evaluate their performance, errors against images segmented manually were measured and ROC curves were determined.
Results:The results show that the proposed methods self-adapt to the different image objects characteristics. An improvement of the accuracy is obtained.Conclusions: This analysis will contribute in images segmentation where complexity of the objects is high.
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