In this talk we present a Hidden Markov Markov for automatic karyotyping. Previously, we demonstrated that this method is robust in the presence of different types of metaphase spreads, truncation of chromosomes, and minor chromosome abnormalities, and that it gives results superior to neural network on standard data sets. In this work we evaluate it on a data set consisting of a mix of chromosomes obtained from blood, amniotic fluid and bone marrow specimens. The method is shown to be robust on this mixed set of data as well as giving far superior results than that obtained by neural networks.
@inproceedings{CoBeLeCh01,
author = {J. M. Conroy and R. L. Becker, Jr. and W. Lefkowitz and K. L. Christopher and R. B. Surana and T. O'Leary and D. P. O'Leary and T. G. Kolda},
title = {Hidden {Markov} Models for Chromosome Identification},
booktitle = {CBMS 2001: Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems},
venue = {Bethesda, MD},
eventdate = {2001-07-26/2001-07-27},
year = {2001},
doi = {10.1109/CBMS.2001.941764},
}