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In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.

Brain portion extraction from magnetic resonance image (MRI) of human head scan is an important process in medical image analysis. In this paper, we propose a computationally simple and a robust brain segmentation method. This method is based on forming a contour using the intensity values that satisfy a set property and detect the boundary of the brain. After detecting the brain boundary the brain portion is segmented. Experiments were conducted by applying the method on 3 volumes of T1 MRI data set collected from Internet Brain Segmentation Repository(IBSR) and compared the results with that of the popular skull stripping method Brain Extraction Tool (BET). The experimental results show that the proposed algorithm gives better results than that of BET.

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