Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using Fuzzy ART Algorithm

  • Kwang-Baek Kim
  • Sungshin Kim
  • Kwee-Bo Sim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colorimetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy ART algorithm is used to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.


Cervical Cancer Radial Basis Function Neural Network Abnormal Cell Fuzzy Neural Network Cervix Uterus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwang-Baek Kim
    • 1
  • Sungshin Kim
    • 2
  • Kwee-Bo Sim
    • 3
  1. 1.Dept. of Computer EngineeringSilla UniversityBusanKorea
  2. 2.School of Electrical EngineeringPusan National UniversityBusanKorea
  3. 3.School of Electrical and Electronic EngineeringChung-Ang UniversitySeoulKorea

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