A Hybrid Approach for Pap-Smear Cell Nucleus Extraction

  • M. Orozco-Monteagudo
  • Hichem Sahli
  • Cosmin Mihai
  • A. Taboada-Crispi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)


This paper, proposes a two-phases approach for a computer-assisted screening system that aims at early diagnosis of cervical cancer in Pap smear images and accurate segmentation of nuclei. The first phase uses spectral, shape as well as the class membership to produce a nested hierarchical partition (hierarchy of segmentations). The second phase, selects the best hierarchical level based on an unsupervised criterion, and refines the obtained segmentation by classifying the individual regions using a Support Vector Machine (SVM) classifier followed by merging adjacent regions belonging to the same class. The effectiveness of the proposed approach for producing a better separation of nucleus regions and cytoplasm areas is demonstrated using both ground truth data, being manually segmented images by pathologist experts, and comparison with state-of-art methods.


microscopic images cell segmentation watershed SVM classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Orozco-Monteagudo
    • 1
  • Hichem Sahli
    • 2
  • Cosmin Mihai
    • 2
  • A. Taboada-Crispi
    • 1
  1. 1.Universidad Central de Las VillasCuba
  2. 2.Electronics and Informatics Dept.Vrije Universiteit Brussel - ETROBrusselsBelgium

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