A novel hybrid approach to chan-vese algorithm for deformable contour based image segmentation

Image segmentation process is the most important and difficult step in object recognition systems. A number of object segmentation methods based on deformable models, also known as active contour models, and which are used to find object boundaries on images have been proposed. However, the main definitions of these methods depend on: the contour initialization and the correct convergence of the subsequent solution. When the initial contour is not properly positioned, this causes to produce unsuccessful results due to problems such as the inability to converge to the desired result within the expected number of iterations, or getting stuck in local minima during the minimization of the energy function. In this thesis study, an image segmentation method based on the gravitational search algorithm which is a heuristic method, and on the active contour without edges model (Chan-Vese) has been developed to overcome the problem of the contour initialization. The proposed model has been developed by combining the gravitational search algorithm and the Chan-Vese algorithm in a hybrid way. It has been tested on various images, including some images from the Weizmann database and medical images. With the robust structure against the initial contour selection of the proposed model, more efficient results were obtained than the conventional Chan-Vese algorithm. Performance of the novel model has been evaluated in terms of accuracy and efficiency compared with the conventional model.