NDLI: Segmentation of M-FISH Images for improved classification of chromosomes with an adaptive fuzzy c-means clustering algorithm

Content Provider	IEEE Xplore Digital Library
Author	Hongbao Cao Yu-Ping Wang
Copyright Year	2011
Description	Author affiliation: Department of Biostatistics, Tulane University, New Orleans, USA (Yu-Ping Wang) \|\| Department of Biomedical Engineering, Tulane University, New Orleans, USA (Hongbao Cao)
Abstract	An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which models and corrects intensity inhomogeneities caused by microscope imaging system, flairs of targets (chromosomes) and uneven hybridization of DNA. Other than directly simulating the inhomogeneousely distributed intensities over the image, the gain field regulates centers of each intensity cluster. The algorithm has been tested on an M-FISH database that we established, demonstrating improved performance in both segmentation and classification. When compared with other fuzzy c-means clustering based algorithms and a recently reported region-based segmentation and classification algorithm, our method gave the lowest segmentation and classification error, which will contribute to improved diagnosis of genetic diseases and cancers.
Starting Page	1442
Ending Page	1445
File Size	292903
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Biological cells Classification algorithms Clustering algorithms Pixel Accuracy Databases background correction Adaptive fuzzy c-means clustering image segmentation chromosome image classification
Content Type	Text
Resource Type	Article

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