Classification of homologous human chromosomes using mutual information maximization

Title	Classification of homologous human chromosomes using mutual information maximization
Publication Type	Conference Paper
Year of Publication	2001
Authors	Mousavi, P., S. S. Fels, R. K. Ward, and P. M. Lansdorp
Conference Name	Image Processing, 2001. Proceedings. 2001 International Conference on
Pagination	845 -848 vol.2
Date Published	oct.
Keywords	cellular biophysics, feature extraction, homologous human chromosome classification, image classification, medical image processing, multi-feature analysis, mutual information maximization, neural nets, optimisation, unsupervised learning, unsupervised neural network architecture
Abstract	Multi-feature analysis of human chromosome images is a major step towards classification of homologous chromosomes. An automatic quantitative classification method is proposed for homolog differentiation using multiple features. This method is based on mutual information maximization applied to an unsupervised neural network architecture. The neural network consists of separate modules which are trained to classify homologs using independent features. Mutual information is then maximized between the outputs of the modules forcing them to produce the same classification results, for a given chromosome. The proposed method was successfully applied to classify homologs of chromosome 16 with 100% accuracy
URL	http://dx.doi.org/10.1109/ICIP.2001.958626
DOI	10.1109/ICIP.2001.958626

The University of British Columbia