Active contour model based edge restriction and attraction field regularization for brain MRI segmentation

摘要

Constructing 3D models of the object of interest from brain MRI is useful in numerous biomedical imaging application. In general, the construction of the 3D models is generally carried out according to the contours obtained from a 2D segmentation of each MR slice, so the equality of the 3D model strongly depends on the precision of the segmentation process. Active contour model is an effective edge-based method in segmenting an object of interest. However, its application, which segment boundary of anatomical structure of brain MRI, encounters many difficulties due to undesirable properties of brain MRI, for example complex background, intensity inhomogeneity and discontinuous edges. This paper proposes an active contour model to solve the problems of automatically segmenting the object of interest from a brain MRI. In this proposed algorithm, a new method of calculating attraction field has been developed. This method is based on edge restriction and attraction field regularization. Edge restriction introduces prior knowledge about the object of interest to free contours of being affected by edges of other anatomical structures or spurious edges, while attraction field regularization enables our algorithm to extract boundary correctly even at the place, where the edge of object of interest is discontinuous, by diffusing the edge information gotten after edge restriction. When we apply this proposed algorithm to brain MRI, the result shows this proposed algorithm could overcome those difficulties we mentioned above and convergence to object boundary quickly and accurately.