Cytological low-quality image segmentation using nonlinear regression, K-means and watershed

Abstract

Since 1950, conventional cytology uses glass slides for microscopic analysis of cervical cells, in order to perform Pap Test. Such method yields low-quality images and overlapping cells, which both hampers their analysis and classification. Several countries use a modern method for the realization of Pap test called ThinPrep because it offers high- quality images and overcomes the problem of overlapping cells. ThinPrep facilitated the development of advanced image processing techniques for segmentation and classification of cervical cells. However, this method is not used by most of the developing countries of the world due to its relative high cost. This paper presents an algorithm for segmenting digital images obtained from conventional cytology method on glass slides. The technique usesWatershed Transform and K-Means Clustering in order to find cell markers or seeds. Nonlinear regression is applied as a way to refine the markers and to allow again the Watershed Transform utilization. We apply the technique in 10 glass slides of pap smears with a total of 67 cells. Our proposed technique has a promising performance in terms of accuracy of about 85%.