NDLI: Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Content Provider	IEEE Xplore Digital Library
Author	Choi, H. Castleman, K.R. Bovik, A.C.
Copyright Year	2006
Description	Author affiliation: Dept. of Biomed. Eng., Univ. of Texas, Austin, TX, USA (Choi, H.)
Abstract	Multicolor fluorescence in-situ hybridization (m-fish) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available m-fish systems exhibit misclassifications of multiple pixel regions that are often larger than the actual chromosomal rearrangement. This paper presents a novel unsupervised classification method based on fuzzy logic classification and a prior adjusted reclassification method. Utilizing the chromosome boundaries, the initial classification results improved significantly after the prior adjusted reclassification while keeping the translocations intact. This paper also presents a new segmentation method that combines both spectral and edge information. Ten m-fish images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average.
Starting Page	69
Ending Page	72
File Size	4472719
Page Count	4
File Format	PDF
ISBN	1424404800
ISSN	15224880
DOI	10.1109/ICIP.2006.312360
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Biological cells Humans Maximum likelihood detection Fluorescence Image color analysis Fuzzy logic Optical interferometry Pixel Image edge detection Unsupervised M-FISH Fuzzy-logic Segmentation Chromosome Classification
Content Type	Text
Resource Type	Article

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