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article

Supervised parametric and non-parametric classification of chromosome images

Authors:

J. K. Aggarwal,

K. R. CastlemanAuthors Info & Claims

Pattern Recognition, Volume 38, Issue 8

Pages 1209 - 1223

https://doi.org/10.1016/j.patcog.2004.09.010

Published: 01 August 2005 Publication History

Abstract

This paper describes a fully automatic chromosome classification algorithm for Multiplex Fluorescence In Situ Hybridization (M-FISH) images using supervised parametric and non-parametric techniques. M-FISH is a recently developed chromosome imaging method in which each chromosome is labelled with 5 fluors (dyes) and a DNA stain. The classification problem is modelled as a 25-class 6-feature pixel-by-pixel classification task. The 25 classes are the 24 types of human chromosomes and the background, while the six features correspond to the brightness of the dyes at each pixel. Maximum likelihood estimation, nearest neighbor and k-nearest neighbor methods are implemented for the classification. The highest classification accuracy is achieved with the k-nearest neighbor method and k=7 is an optimal value for this classification task.

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Cited By

Menaka DVaidyanathan S(2022)Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome imagesMultidimensional Systems and Signal Processing10.1007/s11045-022-00819-x33:3(747-768)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11045-022-00819-x
Alkoot FAlqallaf A(2016)Investigating machine learning techniques for the detection of autismInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2016.08004016:2(141-169)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJDMB.2016.080040
Zhao YKong S(2013)Automated classification of touching or overlapping M-FISH chromosomes by region fusion and homolog pairingPattern Analysis & Applications10.1007/s10044-012-0301-y16:1(31-39)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10044-012-0301-y
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Supervised parametric and non-parametric classification of chromosome images
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Information

Published In

cover image Pattern Recognition

Pattern Recognition Volume 38, Issue 8

August, 2005

181 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Pattern Recognition Society © 2005.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2005

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Cited By

Menaka DVaidyanathan S(2022)Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome imagesMultidimensional Systems and Signal Processing10.1007/s11045-022-00819-x33:3(747-768)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11045-022-00819-x
Alkoot FAlqallaf A(2016)Investigating machine learning techniques for the detection of autismInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2016.08004016:2(141-169)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJDMB.2016.080040
Zhao YKong S(2013)Automated classification of touching or overlapping M-FISH chromosomes by region fusion and homolog pairingPattern Analysis & Applications10.1007/s10044-012-0301-y16:1(31-39)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10044-012-0301-y
Karvelis PFotiadis DTsalikakis DGeorgiou I(2009)Enhancement of multichannel chromosome classification using a region-based classifier and vector median filteringIEEE Transactions on Information Technology in Biomedicine10.1109/TITB.2008.200871613:4(561-570)Online publication date: 1-Jul-2009
https://dl.acm.org/doi/10.1109/TITB.2008.2008716
Castañón CFraga JFernandez SGruber Ada F. Costa L(2007)Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus EimeriaPattern Recognition10.1016/j.patcog.2006.12.00640:7(1899-1910)Online publication date: 1-Jul-2007
https://dl.acm.org/doi/10.1016/j.patcog.2006.12.006

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