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

Dzung Dinh Nguyen, Long Thanh Ngo, Junzo Watada

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    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
    Volume27
    Issue number6
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    In Situ Hybridization
    Fuzzy C-means Clustering
    Chromosomes
    Chromosome
    Segmentation
    Fuzzy C-means Algorithm
    Pixel
    Pixels
    Interval
    DNA
    Fuzzy C-means
    Mask
    Labels
    Masks
    Classify
    Color

    Keywords

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

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Engineering(all)
    • Statistics and Probability

    Cite this

    A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation. / Nguyen, Dzung Dinh; Ngo, Long Thanh; Watada, Junzo.

    In: Journal of Intelligent and Fuzzy Systems, Vol. 27, No. 6, 2014, p. 3111-3122.

    Research output: Contribution to journalArticle

    Nguyen, Dzung Dinh ; Ngo, Long Thanh ; Watada, Junzo. / A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation. In: Journal of Intelligent and Fuzzy Systems. 2014 ; Vol. 27, No. 6. pp. 3111-3122.
    @article{4724d7bb1ec845c8b228f6e5d771e288,
    title = "A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation",
    abstract = "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.",
    keywords = "genetic algorithms, image segmentation, MFISH, Type-2 fuzzy C-neans clustering",
    author = "Nguyen, {Dzung Dinh} and Ngo, {Long Thanh} and Junzo Watada",
    year = "2014",
    doi = "10.3233/IFS-141268",
    language = "English",
    volume = "27",
    pages = "3111--3122",
    journal = "Journal of Intelligent and Fuzzy Systems",
    issn = "1064-1246",
    publisher = "IOS Press",
    number = "6",

    }

    TY - JOUR

    T1 - A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation

    AU - Nguyen, Dzung Dinh

    AU - Ngo, Long Thanh

    AU - Watada, Junzo

    PY - 2014

    Y1 - 2014

    N2 - 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.

    AB - 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.

    KW - genetic algorithms

    KW - image segmentation

    KW - MFISH

    KW - Type-2 fuzzy C-neans clustering

    UR - http://www.scopus.com/inward/record.url?scp=84915806142&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84915806142&partnerID=8YFLogxK

    U2 - 10.3233/IFS-141268

    DO - 10.3233/IFS-141268

    M3 - Article

    AN - SCOPUS:84915806142

    VL - 27

    SP - 3111

    EP - 3122

    JO - Journal of Intelligent and Fuzzy Systems

    JF - Journal of Intelligent and Fuzzy Systems

    SN - 1064-1246

    IS - 6

    ER -