Parametric active contour model by using the honey bee mating optimization

In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time.