Multiresolutional graph cuts for brain extraction from MR images

This paper presents a multiresolutional brain extraction framework which utilizes graph cuts technique to classify head magnetic resonance (MR) images into brain and non-brain regions. Starting with an over-extracted brain region, we refine the segmentation result by trimming non-brain regions in a coarse-to-fine manner. The extracted brain at the coarser level will be propagated to the finer level to estimate foreground/background seeds as constraints. The short-cut problem of graph cuts is reduced by the proposed pre-determined foreground from the coarser level. In order to consider the impact of the intensity inhomogeneities, we estimate the intensity distribution locally by partitioning volume images of each resolution into different numbers of smaller cubes. The graph cuts method is individually applied for each cube. Compared with four existing methods, the proposed method performs well in terms of sensitivity and specificity in our experiments for performance evaluation.