Automatic segmentation of cervical cells in Pap smear images

Abstract

In the context of medical diagnosis by image analysis, segmentation is the most critical step in image processing. The problem of image segmentation has been studied for years and many methods have been suggested in the literature. However, there is not yet any automatic method able to correctly process any type of image. In this work, we present an automated method for cell segmentation in Pap smear images. The automatic analysis of Pap smear images is one of the most interesting fields in medical image processing. The object of this paper is to present the strategy of the first part of the system segmentation. It is based on a segmentation of color images tested with different classical color spaces, namely RGB, L*a*b, HSV, and YCbCr, to select the best color space using k-means clustering to separate groups of objects. The k means clustering treats each object as having a location in space. The method is aimed at developing an automated Pap smear analysis system which can help cytotechnologists reduce examination time in pap screening process.