A Scale-Space Based Approach for Deformable Contour Optimization

Abstract

Multiresolution techniques are often used to shorten the ex- ecution times of dynamic programming based deformable contour op- timization methods by decreasing the image resolution. However, the speedup comes at the expense of contour optimality due to the loss of details and insufficient usage of the external energy in decreased res- olutions. In this paper, we present a new scale-space based technique for deformable contour optimization, which achieves faster optimization times and performs better than the current multiresolution methods. The technique employs a multiscale representation of the underlying images to analyze the behavior of the external energy of the deformable contour with respect to the change in the scale dimension. The result of this anal- ysis, which involves information theoretic comparisons between scales, is used in segmentation of the original images. Later, an exhaustive search on these segments is carried out by dynamic programming to optimize the contour energy. 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 present the results of our contour tracking experiments performed on medical images. We also demonstrate the efficiency and the performance of our system by quantitatively comparing the results with the multires- olution methods, which confirm the effectiveness and the accuracy of our method.

This work was supported by Grant No. R01 DC01758 from NIH and Grant No. IRI 961924 from NSF.