Paper Info
Title
Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm
Abstract
Classification of homologous chromosomes is essential to advanced studies of cancer genetics. Centromere intensities are believed to be an important differentiating feature between homologs. Therefore, segmentation of centromeres is a major step toward the realization of homolog classification. This paper describes an iterative fuzzy algorithm which successfully segments centromeres from images of human chromosomes prepared using fluorescence in-situ hybridization technique. The algorithm is based on assigning a fuzzy membership value to each pixel in the centromere image. An iterative algorithm then updates and minimizes a defined error function. Chromosome 22, a highly heteromorphic chromosome, is used to verify the centromere segmentation method. Homologs of this chromosome are classified based on their segmented centromere intensities as well as their morphological differences. The classification results of these two methods agree completely and are used to validate our developed algorithm.
Year
DOI
Venue
2002
10.1109/10.991164
Biomedical Engineering, IEEE Transactions
Keywords
Field
DocType
cancer,fuzzy logic,genetics,image segmentation,iterative methods,medical image processing,optical microscopy,algorithm validation,centromere segmentation,fluorescence in-situ hybridization technique,fuzzy membership value,heteromorphic chromosome,homolog classification,human chromosome images,iterative fuzzy algorithm,morphological differences,pixel,segmented centromere intensities
Computer vision,Chromosome,Iterative method,Segmentation,Computer science,Fuzzy logic,Algorithm,Image segmentation,Centromere,Karyotype,Artificial intelligence,Chromosome 22
Journal
Volume
Issue
ISSN
49
4
0018-9294
Citations 
PageRank 
References 
3
0.41
0
Authors
4
Name
Order
Citations
PageRank
P. Mousavi1425.62
Rabab K Ward21440135.88
Fels, S.S.313919.30
Sameti, M.430.41