Mean-shift based segmentation of cell nuclei in cervical PAP-smear images

Segmentation of cell nuclei in PAP-smear cervical images is of preeminent importance in computer-aided-diagnostic screening technique for cervical cancer. This paper proposes a novel nuclei segmentation approach which builds upon the mean-shift method. The mean-shift method is applied on the cell images which first undergo a decorrelation-stretch contrast enhancement. The results of mean-shift based approach is refined further using morphological operations. We have validated results of segmentation on dataset which includes 900 images with the given ground truth. We demonstrate that our simple and efficient approach yields high validation rate on a large image dataset. In addition, we also show encouraging visual results on another set of more complex real images.