Cervical Cell Classification Using Features Related to Morphometry and Texture of Nuclei

  • Juan Valentín Lorenzo-Ginori
  • Wendelin Curbelo-Jardines
  • José Daniel López-Cabrera
  • Sergio B. Huergo-Suárez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

The Papanicolaou test is used for early prediction of cervical cancer. Computer vision techniques for automating the microscopy analysis of cervical cells in this test have received great attention. Cell segmentation is needed here in order to obtain appropriate features for classification of abnormal cells. However, accurate segmentation of the cell cytoplasm is difficult, due to cell overlapping and variability of color and intensity. This has determined a growing interest in classifying cells using only features from the nuclei, which are easier to segment. In this work, we classified cells in the pap-smear test using a combination of morphometric and Haralick texture features, obtained from the nucleus gray-level co-occurrence matrix. A comparison was made among various classifiers using these features and data dimensionality reduction through PCA. The results obtained showed that this combination can be a promising alternative in order to automate the analysis of cervical cells.

Keywords

Papanicolaou test features texture dimensionality reduction classifiers 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan Valentín Lorenzo-Ginori
    • 1
  • Wendelin Curbelo-Jardines
    • 1
  • José Daniel López-Cabrera
    • 1
  • Sergio B. Huergo-Suárez
    • 1
  1. 1.Center for Studies on Electronics and Information TechnologiesUniversidad Central “Marta Abreu” de Las Villas (UCLV)Cuba

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