Hidden Markov models for chromosome identification

Author

Conroy, John M. ; Becker, Robert L., Jr. ; Lefkowitz, William ; Christopher, Kewi L. ; Surana, Rawatmal B. ; O´Leary, Timothy J. ; O´Leary, Dianne P. ; Kolda, Tamara G.

Author_Institution

Center for Comput. Sci., Inst. for Defense Analyses, Bowie, MD, USA

fYear

2001

fDate

2001

Firstpage

473

Lastpage

477

Abstract

Presents a hidden Markov model 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 networks on standard data sets. In this paper, 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 those obtained by neural networks

Keywords

biology computing; blood; bone; cellular biophysics; feature extraction; hidden Markov models; medical image processing; neural nets; amniotic fluid; automatic karyotyping; blood; bone marrow specimens; chromosome identification; chromosome truncation; hidden Markov model; medical image processing; medical signal processing; medical software system; metaphase spreads; minor chromosome abnormalities; mixed data set; standard data sets; Amniotic fluid; Biological cells; Biological neural networks; Blood; Bones; Hidden Markov models; Military computing; Neural networks; Pathology; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on

Conference_Location

Bethesda, MD

ISSN

1063-7125

Print_ISBN

0-7695-1004-3

Type

conf

DOI

10.1109/CBMS.2001.941764

Filename

941764