A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation

Dzung Dinh Nguyen, Long Thanh Ngo*, Junzo Watada

*この研究の対応する著者

    研究成果: Article査読

    7 被引用数 (Scopus)

    抄録

    Multiplex Fluorescent In Situ Hybridization (M-FISH) is a multi-channel chromosome image generating technique that allows colors of the human chromosomes to be distinguished. In this technique, all chromosomes are labelled with 5 fluors and a fluorescent DNA stain called DAPI (4 in, 6-Diamidino-2-phenylindole) that attaches to DNA and labels all chromosomes. Therefore, a M-FISH image consists of 6 images, and each image is the response of the chromosome to a particular fluor. In this paper, we propose a genetic interval type-2 fuzzy c-means (GIT2FCM) algorithm, which is developed and applied to the segmentation and classification of M-FISH images. Chromosome pixels from the DAPI channel are segmented by GIT2FCM into two clusters, and these chromosome pixels are used as a mask for the remaining five channels. Then, the GIT2FCM algorithm is applied to classify the chromosome pixels into 24 classes, which correspond to the 22 pairs of homologous chromosomes and two sexual chromosomes. The experiments performed using the M-FISH dataset show the advantages of the proposed algorithm.

    本文言語English
    ページ(範囲)3111-3122
    ページ数12
    ジャーナルJournal of Intelligent and Fuzzy Systems
    27
    6
    DOI
    出版ステータスPublished - 2014

    ASJC Scopus subject areas

    • 人工知能
    • 工学(全般)
    • 統計学および確率

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