Image segmentation algorithm of touching rice kernels based on active contour model

A new segmentation method based on active contour model was proposed in this paper and was applied to segmenting touching rice kernels in an image. Firstly, the local minimums were detected in the Euclidean distance transform of the binary image of the rice kernels. Those local minimums were merged by the morphologic dilation operator, so that one kernel corresponded to one region. Then, the edges of those regions were taken as initial curves and converged to the true boundary of the rice kernels in the image under the guidance of active contour model. The experimental results showed that the proposed segmentation algorithm based on active contour model got desirable segmentation. Its segmentation accuracy for round glutinous rice, non-glutinous rice, long glutinous rice and black rice reached 93.4%, 92.4%, 88.0% and 90.4%, respectively. That was, its overall accuracy achieved 91.05%, which was 26.7% more than that of the watershed based method. Therefore, the algorithm based on active contour model provides an effective means to separate the touching rice kernels in an image.