Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap Smear Images

    Research output: Contribution to conferencePaper

    20 Citations (Scopus)

    Abstract

    Precise segmentation of Pap smear cell nucleus is crucial for early diagnosis of cervical cancer. This task is particularly challenging because of cell overlapping, inconsistent staining, poor contrast and other imaging artifacts. In this study, a novel method is proposed to segment cell nucleus from overlapping Pap smear cell images. The proposed technique introduces a circular shape function (CSF) to increase the robustness of Pap cell nucleus segmentation using fuzzy c-means clustering. CSF imposes a shape constrain over the formed clusters, while improves the boundary of the nucleus. The shape function helps to differentiate the pixels having similar intensity value but located in different spatial regions. The method is evaluated using Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 dataset and compared with the traditional FCM clustering and recently published state-of-the-art methods. Both qualitative and quantitative measures indicate that the new technique performs favorably with others.

    Original languageEnglish
    DOIs
    Publication statusPublished - 2016
    Event2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) -
    Duration: 30 Nov 2016 → …

    Conference

    Conference2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Period30/11/16 → …

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  • Cite this

    Saha, R., Bajger, M., & Lee, G. (2016). Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap Smear Images. Paper presented at 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), . https://doi.org/10.1109/DICTA.2016.7797086