Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm.
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.
Mousavi
Parvin
P
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
Ward
Rabab Kreidieh
RK
Fels
Sidney S
SS
Sameti
Mohammad
M
Lansdorp
Peter M
PM
D000465:Algorithms; D002503:Centromere / Q000648:ultrastructure*; D002892:Chromosomes, Human, Pair 22 / Q000235:genetics*; D017143:Fuzzy Logic; D006801:Humans; D017404:In Situ Hybridization, Fluorescence; D008677:Metaphase; D008856:Microscopy, Fluorescence