Medical Image Segmentation Using Descriptive Image Features

Segmentation of medical images is an important component for diagnosis and treatment of diseases using medical imaging technologies. However, automated accurate medical image segmentation is still a challenge due to the difculties in nding a robust feature descriptor to describe the object boundaries in medical images. In this paper, a new normal vector feature prole (NVFP) is proposed to describe the local image information of a contour point by concatenating a series of local region descriptors along the normal direction at that point. To avoid trapping by false boundaries caused by nonboundary image features, a modied scale invariant feature transform (SIFT) descriptor is developed. The number and locations of sample points for building NVFP are determined for each contour point, which are constrained by the neighboring anatomical structures and the statistical consistency of the training features. NVFP is incorporated into a model based method for image segmentation. The performance of our proposed method was demonstrated by segmenting prostate MR images. The segmentation results indicated that our method can achieve better performance compared with other existing methods