Supervised parametric and non-parametric classification of chromosome images

被引:38
|
作者
Sampat, MP
Bovik, AC
Aggarwal, JK [1 ]
Castleman, KR
机构
[1] Univ Texas, Comp & Vis Res Ctr, Dept Elect & Comp Engn, Austin, TX 78712 USA
[2] Univ Texas, Lab Image & Video Engn, Dept Biomed Engn, Austin, TX 78712 USA
[3] Univ Texas, Lab Image & Video Engn, Dept Elect & Comp Engn, Austin, TX 78712 USA
[4] LLC, Adv Digital Imaging Res, League City, TX 77573 USA
关键词
M-FISH; nearest neighbor; k-nearest neighbor; maximum likelihood estimation; karyotyping;
D O I
10.1016/j.patcog.2004.09.010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1209 / 1223
页数:15
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