<title>Progress in the robust automated segmentation of real cell images</title>

Abstract

We propose a collection of robust algorithms for the segmentation of cell images from Papanicolaou stained cervical smears ( "Pap" smears) . This problem is deceptively difficult and often results on laboratory datasets do not carry over to real world data. Our approach is in 3 parts. First, we segment the cytoplasm from the background using a novel method based on the Wilson and Spann multi-resolution framework. Second, we segment the nucleus from the cytoplasm using an active contour method, where the best contour is found by a global minimisation method. Third, we implement a method to determine a confidence measure for the segmentation of each object. This uses a stability criterion over the regularisation parameter A in the active contour. We present the results of thorough testing of the algorithms on large numbers of cell images. A database of 20,130 images is used for the segmentation tests and 18,718 images for the robustness tests.