Capabilities of nonlinear gradient and a thresholding algorithm for the segmentation of Papanicolaou-stained cervical cells.

The first and most important task of automatic high-resolution cell image analysis is the segmentation of the scanned cells. Different methods of cell segmentation have been developed, but a comparison of the capabilities of such algorithms has not been done. This study evaluated two different segmentation methods for cell images, namely, a nonlinear gradient algorithm with a subsequent tracing method and a thresholding algorithm based on the information from three histograms with a subsequent nonlinear cleaning of the binary thresholded images. The same Papanicolaou-stained cell data base was used in both methods. Automatic segmentation of nucleus and cytoplasm was performed, and a comparison with visually segmented areas of the nucleus and cytoplasm was carried out. The difference between the visual method and the automatic segmentation method by thresholding is discussed in terms of classification results.