A coarse-to-fine deformable contour optimization framework

This paper introduces a novel coarse-to-fine deformable contour optimization framework, which is composed of two main components. The first component uses scale-space and information theories to produce a coarser representation of the input image to be used in a coarse-to-fine optimization scheme. The employment of information theory ensures that maximal image information is propagated to the coarse images and employment of scale spaces provides a mechanism to change the image coarseness locally based on the deformable contour model definition. The second component of this framework uses a novel combination of dynamic programming and gradient descent methods to optimize the contour energy on coarser representations and then use the obtained coarse contour positions in finer optimizations. The motivation in using a combination of dynamic programming and gradient descent method is to take advantage of each method's efficiency and avoid their drawbacks. In order to verify the performance of this framework, we constructed a deformable contour model for the spatiotemporal tracking of closed contours and optimized the model energy under this framework. Experiments on this system performed using synthetic images and real world echocardiographic sequences demonstrated the effectiveness and practicality of this framework.