Automatic detection of colloidal particles based on a shape regularized integrated active contour model

Manual segmentation of single colloidal particle in suspension encounters a bottleneck when a number of defocused particles simultaneously exist in an image. In this paper, we describe an image processing algorithm for extracting individual particle from digitized microscope images of colloidal suspensions. We propose a particle detection and location solution using a shape regularized integrated active contour model (ACM). Compared with existing methods where active contour models are not applied well to deal with multiple objects in complicated background, the proposed approach can automatically identify and locate multiple particles by combining characteristics of the particles such as shape, boundary and region. A regularization term is defined by prior information of specific shape, which is able to drive the shape of evolving curve toward the shape prior gradually. To locate the centers of the particles, the Hough transform is applied. Experimental results using polystyrene beads as sample particles reveal that the method has high efficiency and ability to deal with colloidal particles.