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Morphological characteristics of cervical cells for cervical cancer diagnosis

Conference Paper


Abstract


  • This paper investigates cervical cancer diagnosis based on the morphological

    characteristics of cervical cells. The developed algorithms cover several

    steps: pre-processing, image segmentation, nuclei and cytoplasm detection, feature

    calculation, and classification. The K-means clustering algorithm based on colour

    segmentation is used to segment cervical biopsy images into five regions: background,

    nuclei, red blood cell, stroma and cytoplasm. The morphological characteristics

    of cervical cells are used for feature extraction of cervical histopathology

    images. The cervical histopathology images are classified using four well known

    discriminatory features: 1) the ratio of nuclei to cytoplasm, 2) the diameter of nuclei,

    3) the shape factor and 4) the compactness of nuclei. Finally, the images are

    analysed and classified into appropriate classes. This method is utilised to classify

    the cervical biopsy images into normal, pre-cancer (Cervical Intraepithelial Neoplasia

    (CIN)1, CIN2, CIN3) and malignant.

Publication Date


  • 2012

Citation


  • Rahmadwati, , Naghdy, G., Ros, M. & Todd, C. 2012, 'Morphological characteristics of cervical cells for cervical cancer diagnosis', in F. Gaol & Q. Nguyen (eds), Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science, Volume 2, Springer, Heidelberg, pp. 235-243.

Scopus Eid


  • 2-s2.0-84862849174

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2110

Has Global Citation Frequency


Start Page


  • 235

End Page


  • 243

Place Of Publication


  • Heidelberg

Abstract


  • This paper investigates cervical cancer diagnosis based on the morphological

    characteristics of cervical cells. The developed algorithms cover several

    steps: pre-processing, image segmentation, nuclei and cytoplasm detection, feature

    calculation, and classification. The K-means clustering algorithm based on colour

    segmentation is used to segment cervical biopsy images into five regions: background,

    nuclei, red blood cell, stroma and cytoplasm. The morphological characteristics

    of cervical cells are used for feature extraction of cervical histopathology

    images. The cervical histopathology images are classified using four well known

    discriminatory features: 1) the ratio of nuclei to cytoplasm, 2) the diameter of nuclei,

    3) the shape factor and 4) the compactness of nuclei. Finally, the images are

    analysed and classified into appropriate classes. This method is utilised to classify

    the cervical biopsy images into normal, pre-cancer (Cervical Intraepithelial Neoplasia

    (CIN)1, CIN2, CIN3) and malignant.

Publication Date


  • 2012

Citation


  • Rahmadwati, , Naghdy, G., Ros, M. & Todd, C. 2012, 'Morphological characteristics of cervical cells for cervical cancer diagnosis', in F. Gaol & Q. Nguyen (eds), Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science, Volume 2, Springer, Heidelberg, pp. 235-243.

Scopus Eid


  • 2-s2.0-84862849174

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2110

Has Global Citation Frequency


Start Page


  • 235

End Page


  • 243

Place Of Publication


  • Heidelberg