3D Model-Based Segmentation of 3D Biomedical Images

Abstract

A central task in biomedical image analysis is the segmentation and quantification of 3D image structures. A large variety of segmentation approaches have been proposed including approaches based on different types of deformable models. A main advantage of deformable models is that they allow incorporating a priori information about the considered image structures. In this contribution we give a brief overview of often used deformable models such as active contour models, statistical shape models, and analytic parametric models. Moreover, we present in more detail 3D analytic parametric intensity models, which enable accurate and robust segmentation and quantification of 3D image structures. Such parametric models have been successfully used in different biomedical applications, for example, for the localization of 3D anatomical point landmarks in 3D MR and CT images, for the quantification of vessels in 3D MRA and CTA images, as well as for the segmentation of cells and subcellular structures in 3D microscopy images.