An accurate and robust skull stripping method for 3-D magnetic resonance brain images

Segmentation of brain region from an MR volume is an essential prerequisite for any automatic medical image processing application as it increases both speed and accuracy of the diagnosis in manifold. Due to material heterogeneity and resolution limitation of imaging devices, the MR image introduces graded intensity of tissues within the brain region. Moreover, it incurs the blurring effect at the brain surface. In spite of these artifacts, all the tissues of brain region of an MR image are perceived to be hanged together within the brain. In this regard, this paper introduces an accurate and robust skull stripping algorithm, termed as ARoSi. It is based on a novel concept, called rough-fuzzy connectedness, introduced in this paper. In the proposed method, the connectedness of a voxel to the brain region is determined by its degree of belongingness to the brain region as well as the degree of adjacency to the brain. Moreover, the proposed ARoSi algorithm considers the local spatial information of the voxel of interest, which reduces the effect of noise, and in turn, helps to improve the performance of the proposed method. Finally, the performance of the proposed ARoSi algorithm, along with a comparison with other state-of-the-art algorithms, is demonstrated on T1-weighted 3-D brain MR volumes obtained from four different data sets. The experiments show that the performance of ARoSi is consistent across all the four data sets, including diseased data sets. The proposed algorithm achieves the highest mean Dice coefficient of value 0.951 for all the volumes of four different data sets, among six existing brain extraction methods.