The work focuses on a unique medical repository of digital cervicographic images (Cervigrams) collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the os), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.
Bibliographical noteFunding Information:
Manuscript received June 19, 2008; revised September 19, 2008. First published October 31, 2008; current version published February 25, 2009. 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 (LHNCBC).Asterisk indicates corresponding author. *H. Greenspan is with the Department of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Ramat-Aviv 69978, Israel (e-mail: firstname.lastname@example.org).
- Cervical cancer
- Curvature features
- Image segmentation
- Landmark extraction
- Medical image analysis