Conditional-mean initialization using neighboring objects in deformable model segmentation
Ja-Yeon Jeong, Joshua V. Stough, J. Steve Marron, Stephen M. Pizer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Most model-based segmentation methods find a target object in a new image by constructing an objective function and optimizing it using a standard minimization algorithm. In general, the objective function has two penalty terms: 1) for deforming a template model and 2) for mismatch between the trained image intensities relative to the template model and the observed image intensities relative to the deformed template model in the target image. While it is difficult to establish an objective function with a global minimum at the desired segmentation result, even such an objective function is typically non-convex due to the complexity of the intensity patterns and the many structures surrounding the target object. Thus, it is critical that the optimization starts at a point close to the global minimum of the objective function in deformable model-based segmentation framework. For a segmentation method in maximum a posteriori framework a good objective function can be obtained by learning the probability distributions of the population shape deformations and their associated image intensities because each penalty term can be simplified to a squared function of some distance metric defined in the shape space. The mean shape and intensities of the learned probability distributions also provide a good initialization for segmentation. However, a major concern in estimating the shape prior is the stability of the estimated shape distributions from given training samples because the feature space of a shape model is usually very high