Automatic initialization for active contour models based on particle swarm optimization and application to medical images

Abstract

The active contour models (ACMs) are one of the most widely used techniques in image segmentation, localization and object tracking. Although there are several existing updated versions of ACMs, in which most of the models do not converge to the desired results in images due to complex background and depends on the initial placement of contour. Among the methods based on the level set, the Local Gaussian Distribution Fitting (LGDF) Energy and the Local Binary Fitting (LBF) Energy are successful algorithms, that suffers from image contours and initial position selection, which are the considerable demerits of the models. To overcome these adversities, we present an efficient and socially-inspired population based stochastic algorithm called particle swarm contour search (PSCS) which is the modified version of particle swarm optimization (PSO) algorithm, considered to be one of the most important optimization methods in swarm intelligence. Firstly, we apply smoothing filters on image to remove high intensities noise. Secondly, we utilized the PSCS algorithm to find the dominant points around the object’s boundaries. The PSCS selects some extra points in different parts of the image rather than the required object, such points are removed in our post PSCS step by utilizing different morphological operations. Furthermore, we calculate the center position and radius of the object for initial contour with the help of points generated using PSCS. The experimental outcome of the segmentations indicate that our proposed approach is automatic and fast for its initialization and successfully segment the desire object in medical images.