Quantitative assessment of Pap smear cells by PC-based cytopathologic image analysis system and support vector machine

  • Authors:

  • Po-Chi Huang;Yung-Kuan Chan;Po-Chou Chan;Yung-Fu Chen;Rung-Ching Chen;Yu-Ruei Huang

  • Affiliations:

  • Department of Pathology, Taichung Hospital, Department of Health, Taichung, Taiwan;Department of Management Information Systems, National Chung Hsin University, Taichung;Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung;Department of Health Services Administration, China Medical University, Taichung;Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan

  • Venue:
  • ICMB'08 Proceedings of the 1st international conference on Medical biometrics
  • Year:
  • 2008

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Abstract

Cytologic screening has been widely used for controlling the prevalence of cervical cancer. Errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims at designing a cellular image analysis system based on feasible and available software and hardware for a routine cytologic laboratory. Totally 1814 cellular images from the liquid-based cervical smears with Papanicolaou stain in 100x, 200x, and 400x magnification were captured by a digital camera. Cell images were reviewed by pathologic experts with peer agreement and only 503 images were selected for further study. The images were divided into 4 diagnostic categories. A PC-based cellular image analysis system (PCCIA) was developed for computing morphometric parameters. Then support vector machine (SVM) was used to classify signature patterns. The results show that the selected 13 morphometric parameters can be used to correctly differentiate the dysplastic cells from the normal cells (p