Multispectral integration for segmentation of chromosome images

Abstract

This paper presents a methodology and results for multispectral integration in chromosome images by learning disparate models from each channel for pixel classification. The objective is the classification of pixels to identify each of the individual chromosomes. The methodology is based on a modular structure consisting of multiple classifiers, each of which solves the problem independently based on its input observations. Each classifier module is trained to detect distinct regions and a higher order decision integrator collects evidence from each of the modules to delineate a final region. A Bayesian realization of the framework is developed, where each classifier module represents the conditional probability density function. Results of classification on a public database are presented. © Springer-Verlag Berlin Heidelberg 2005.