A stochastic approach for automated brain MRI segmentation

Abstract

Abstract This paper presents an approach to segment lesions from brain magnetic resonance images in a fully automatic manner. The proposed idea leverages the strength of classical random walker algorithm and graph cut optimization technique in a single framework. We demonstrate that formulating a “prior” from a stochastic model can ameliorate the need of manually selected seed selection process in the random walker framework, thus making the algorithm fully automatic in a generic manner. By analytically solving a linear system of equations in the random walk process, initial labelling ∈ [0, 1] of each pixel in the image are computed to obtain the likelihood probability. These probabilities are then used to compute the likelihood for the data fidelity term in the energy function to compute the final segmentation, which is minimized by min cut max flow algorithm. Experimental results show the superiority of the proposed method over the state‐of‐the‐art techniques on publicly available datasets.

Keywords

• Algebra, set theory, and graph theory
• Probability theory, stochastic processes, and statistics
• Biophysics of neurophysiological processes
• Medical magnetic resonance imaging and spectroscopy
• Patient diagnostic methods and instrumentation
• Other topics in statistics