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

Dzung Dinh Nguyen, Long Thanh Ngo*, Junzo Watada

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)3111-3122
    Number of pages12
    JournalJournal of Intelligent and Fuzzy Systems
    Issue number6
    Publication statusPublished - 2014


    • genetic algorithms
    • image segmentation
    • MFISH
    • Type-2 fuzzy C-neans clustering

    ASJC Scopus subject areas

    • Artificial Intelligence
    • General Engineering
    • Statistics and Probability


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