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Summary: Supervised Parametric and Non-Parametric
Classification of Chromosome Images
M. P. Sampat a
A. C. Bovik b
J. K. Aggarwal b
K. R. Castleman c
aDept. Of Biomedical Engineering,The University of Texas at Austin,TX 78712
bDept. Of Electrical and Computer Engineering,The University of Texas at
Austin,TX 78712
cAdvanced Digital Imaging Research, LLC, League City,Texas 77573
Abstract
This paper describes a fully automatic chromosome classification algorithm for Mul-
tiplex Fluorescence In-Situ Hybridization(M-FISH) images using supervised para-
metric 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
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