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A Data Driven Approach to Cervigram Image Analysis and Classification

  • Edward Kim
  • Xiaolei Huang
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 6)

Abstract

Cervical cancer is one of the leading causes of death for women worldwide. Early detection of cervical cancer is possible through regular screening; however, in developing countries, screening and treatment options are limited due to poor (or lack of) resources. Fortunately, low cost screening procedures utilizing visual inspection after the application of acetic acid in combination with low cost DNA tests to detect HPV infections have been shown to reduce the lifetime risk of cervical cancer by nearly 30 %. To assist in this procedure, we developed an automatic, data centric system for cervigram (photographs of the cervix) image analysis. In the first step of our algorithm, our system utilizes nearly a thousand annotated cervigram images to automatically locate a cervix region of interest. Next, by utilizing both color and texture features extracted from the cervix region of interest on several thousand cervigrams, we show that our system is able to perform a binary classification of disease grading on cervigram images with comparable accuracy to a trained expert. Finally, we analyze and report the effect that the color and texture features have on our end classification result.

Keywords

Cervical Cancer Ground Truth Texture Feature Gaussian Mixture Model Cervix Region 
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.

Notes

Acknowledgements

This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM), and Lister Hill National Center for Biomedical Communications (LH- NCBC). The image and clinical data for this work comes from the National Cancer Institute (NCI) Guanacaste/ALTS projects.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLehigh UniversityBethlehemUSA

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