For 3D model-based approaches, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently remains an open problem. In this paper, we propose a novel, general method for the generation of 3D statistical shape models. Given a set of training 3D shapes, 3D model generation is achieved by 1) building the mean model from the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean model, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. Previous 3D modeling efforts all had severe limitations in terms of the object shape, geometry, and topology. The proposed method is very general without such assumptions and is applicable to any data set.
Liver segmentation is one of the most basic and important parts in computer-aided diagnosis for liver CT. Although various segmentation methods have been proposed for medical imaging, most of them generally do not perform well in segmenting the liver from CT images because of surface features of the liver and difficulty of discrimination from other adjacent organs. In this paper, we propose a new scheme for automatic segmentation of the liver in CT images. The pro-posed scheme is carried out on region-of-interest (ROI) blocks that include regions of the liver with high probabilities. The ROI approach saves unnecessary computational loss in finding the accurate boundary of the liver. The proposed method utilizes the composition of morphological filters with a priori knowledge, such as the general location or the approximate intensity of the liver to detect the initial boundary of the liver. Then, we make the gradient image with the weight of an initial liver boundary and segment the liver region by using an immersion-based watershed algorithm in the gradient image. Finally, a refining process is carried out to acquire a more accurate liver region.