NDLI: Joint segmentation and pairing of multispectral chromosome images

Content Provider	Springer Nature Link
Author	Zhao, Yongqiang Wu, Xiaolin Kong, Seong G. Zhang, Lei
Copyright Year	2011
Abstract	Automated segmentation of touching or overlapping chromosomes in a metaphase image is a critical step for computer-aided chromosomes analysis. Conventional chromosome imaging methods acquire single-band grayscale images, and such a limitation makes the separation of touching or overlapping chromosomes challenging. In the multiplex fluorescence in situ hybridization (M-FISH) technique, each class of chromosomes can bind with a different combination of fluorophores. The M-FISH technique results in multispectral chromosome images, which has distinct spectral signatures. This paper presents a novel automated chromosome analysis method to combine the pixel-level geometric and multispectral information with decision-level pairing information. Our chromosome segmentation method uses the geometric and spectral information to partition the chromosome cluster into three regions. There will be ambiguity when combining these regions into separated chromosomes by using only spectral and geometric information. Then a graph–theoretical pairing method is introduced to resolve any remaining ambiguity of the aforementioned segmentation process. Experimental results demonstrate that the proposed joint segmentation and pairing method outperforms conventional grayscale and multispectral segmentation methods in separating touching and overlapping chromosomes.
Starting Page	497
Ending Page	506
Page Count	10
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	16
Issue Number	4
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2011-10-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multispectral imaging Chromosome image segmentation Shape decomposition Homologue pairing Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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