Adaptive segmentation of cervical smear image based on GVF Snake model

The key of a computer-assisted diagnosis system for screening of cervical cancer is the accurate segmentation of cells. In this paper, an adaptive segmentation algorithm based on GVF Snake model is proposed to separate the nucleus from cervical smear model. We set the parameters of the model, and then use the model to segment the cervical cells based on the initial contour of nuclei. The segmentation results are evaluated, if the results meet the criterion, the segmentation process finishes, otherwise we adjust the parameter which is a weight in energy function when calculating GVF field and perform the segmentation procedure again. Adjusting the parameter by evaluating the segmentation results is an adaptive and sensible method with regard to the automatic segmentation. The experiment results show the effectiveness of the proposed approach in images having inconsistent staining and poor contrast.