Application of particle swarm optimization and snake model hybrid on medical imaging

Abstract

Active contour model has been widely used in image processing applications such as boundary delineation, image segmentation, stereo matching, shape recognition and object tracking. In this paper a novel particle swarm optimization scheme has been introduced to evolve snake over time in a way to reduce time complexity while improving quality of results. Traditional active contour models converge slowly and are prone to local minima due to their complex nature. Various evolutionary techniques including genetic algorithms, particle swarm optimization and predator prey optimization have been successfully employed to tackle this problem. Most of these methods are general problem solvers that, more or less, formulate the snake model equations as a minimization problem and try to optimize it. In contrary, our proposed approach integrates concepts from active contour model into particle swarm optimization so that each particle will represent a snaxel of the active contour. Canonical velocity update equation in particle swarm algorithm is modified to embrace the snake kinematics. This new model makes it possible to have advantages of swarm based searching strategies and active contour principles all together. Aptness of the proposed approach has been examined through several experiments on synthetic and real world images of CT and MRI images of brain and the results demonstrate its promising performance particularly in handling boundary concavities and snake initialization problems.