Statistical Shape Analysis (SSA) is a powerful tool for noninvasive studies of pathophysiology and diagnosis of brain diseases. It also provides a shape constraint for the segmentation of brain structures. There are two key problems in SSA: the representation of shapes and their alignments. The widely used parameterized representations are obtained by preserving angles or areas and the alignments of shapes are achieved by rotating parameter net. However, representations preserving angles or areas do not really guarantee the anatomical correspondence of brain structures. In this paper, we incorporate shape-based landmarks into parameterization of banana-like 3D brain structures to address this problem. Firstly, we get the triangulated surface of the object and extract two landmarks from the mesh, i.e. the ends of the banana-like object. Then the surface is parameterized by creating a continuous and bijective mapping from the surface to a spherical surface based on a heat conduction model. The correspondence of shapes is achieved by mapping the two landmarks to the north and south poles of the sphere and using an extracted origin orientation to select the dateline during parameterization. We apply our approach to the parameterization of lateral ventricle and a multi-resolution shape representation is obtained by using the Discrete Fourier Transform.
White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.