Morphological Characteristics of Cervical Cells for Cervical Cancer Diagnosis

  • Rahmadwati
  • Golshah Naghdy
  • Montse Ros
  • Catherine Todd
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)

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.

Keywords

cervical cancer diagnosis morphological characteristic 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Rahmadwati
    • 1
  • Golshah Naghdy
    • 2
  • Montse Ros
    • 2
  • Catherine Todd
    • 3
  1. 1.Department of Electrical EngineeringBrawijaya UniversityMalangIndonesia
  2. 2.School of Electrical, Computer and Telecommunication EngineeringUniversity of WollongongNew South WalesAustralia
  3. 3.Faculty of Computer Science and EngineeringUniversity of WollongongDubaiUnited Arab Emirates

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