A new multi-level framework for deformable contour optimization

Application of dynamic programming to the deformable contours has many advantages, such as guaranteed optimality and numerical stability. However, long execution times of these methods almost always force researchers to use dynamic programming in combination with multiresolution methods. Multiresolution methods shorten the execution time by subsampling the original images after an application of a smoothing filter. However, this speedup comes at the expense of contour optimality due to the loss of details in the decreased resolution. In this paper, we present a new multi-level framework for deformable contour optimization, which can achieve faster optimization times and performs better than current multiresolution methods. To form the new levels, this method uses a very efficient algorithm to segment the original images with respect to the deformable contour external energy instead of subsampling. An exhaustive search on these segments is carried out by dynamic programming. A novel gradient descent algorithm is employed to find optimal internal energy for large image segments, where the external energy remains constant due to segmentation. We also introduce a new algorithm to pass the contour information more precisely between the levels. We present an analysis of time and performance comparisons with the current multiresolution methods by the experiments done on variety of medical images, which confirmed efficiency and accuracy of our framework.