A skull stripping method using deformable surface and tissue classification

Xiaodong Tao, Ming-Ching Chang

Abstract: Many neuroimaging applications require an initial step of skull stripping to extract the cerebrum, cerebellum, and brain stem. We approach this problem by combining deformable surface models and a fuzzy tissue classification technique. Our assumption is that contrast exists between brain tissue (gray matter and white matter) and cerebrospinal fluid, which separates the brain from the extra-cranial tissue. We first analyze the intensity of the entire image to find an approximate centroid of the brain and initialize an ellipsoidal surface around it. We then perform a fuzzy tissue classification with bias field correction within the surface. Tissue classification and bias field are extrapolated to the entire image. The surface iteratively deforms under a force field computed from the tissue classification and the surface smoothness. Because of the bias field correction and tissue classification, the proposed algorithm depends less on particular imaging contrast and is robust to inhomogeneous intensity often observed in magnetic resonance images. We tested the algorithm on all T1 weighted images in the OASIS database, which includes skull stripping results using Brain Extraction Tool; the Dice scores have an average of 0.948 with a standard deviation of 0.017, indicating a high degree of agreement. The algorithm takes on average 2 minutes to run on a typical PC and produces a brain mask and membership functions for gray matter, white matter, and cerebrospinal fluid. We also tested the algorithm on T2 images to demonstrate its generality, where the same algorithm without parameter adjustment gives satisfactory results.