A comparison of deformable contour methods and model based approach using skeleton for shape recovery from images.

摘要

Image segmentation is the premise of image understanding process. Deformable contour methods (DCMs) are currently the most popular image segmentation approaches and many of them were proposed in past years. In order to understand the strengths and weakness of different DCMs on image segmentation applications, this dissertation provides a qualitative and quantitative comparison of some major DCMs on a set of selected biomedical images. Though they works well on some cases, there are still many very challenging and difficult problems that they can't handle, such as very blur contour segments, complex shape, inhomogeneous interior and inhomogeneous contour region distribution, just to name a few. Model based DCMs are necessary to solve these problems. We present a new model based approach for accurate shape recovery from images by applying a skeleton based shape matching method. The shape matching method consists of two major operations---skeleton extraction and shape model representation, and matching and model detection. For skeleton extraction, a distance transformation based method is employed. The shape model of an object consists of both the skeleton model and the contour segments model, which are used in tandem and in a complementary manner. The skeleton matching algorithm is introduced to match the skeleton of a DCM contour against a set of object skeleton models to select the candidate model and determine the corresponding landmarks on the contours based on their skeleton structure and a similarity function. In shape recovery process, segments obtained from these landmarks are then matched against the detected model segments for errors. For any large error in segments mismatch, a fine-tuning process, which is formulated as a maximization of a posteriori probability, given the contour segments model and image features, is performed for final result. The skeleton based shape matching approach is also amendable for object recognition. The skeleton matching algorithm is illustrated by using a set of animal profile examples. Experimental results of shape recovery from practical applications, such as on MR knee and brain images, are very encouraging.