Cell Image Segmentation for Diagnostic Pathology

Abstract

The colors associated with a digitized specimen representing peripheral blood smear are typically characterized by only a few, non-Gaussian clusters, whose shapes have to be discerned solely from the image being processed. Nonparametric methods such as mode-based analysis [952], are particularly suitable for the segmentation of this type of data since they do not constrain the cluster shapes. This chapter reviews an efficient cell segmentation algorithm that detects clusters in the L*u*v color space and delineates their borders by employing the gradient ascent mean shift procedure [950], [951]. The color space is randomly tessellated with search windows that are moved till convergence to the nearest mode of the underlying probability distribution. After the pruning of the mode candidates, the colors are classified using the basins of attraction. The segmented image is derived by mapping the color vectors in the image domain and enforcing spatial constraints.