title: Automatic detection of anatomical landmarks in uterine cervix images.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Ramat-Aviv 69978, Israel. hayit@eng.tau.ac.il
authors.forename: Hayit
authors.initials: H
authors.lastname: Greenspan
authors.forename: Shiri
authors.initials: S
authors.lastname: Gordon
authors.forename: Gali
authors.initials: G
authors.lastname: Zimmerman
authors.forename: Shelly
authors.initials: S
authors.lastname: Lotenberg
authors.forename: Jose
authors.initials: J
authors.lastname: Jeronimo
authors.forename: Sameer
authors.initials: S
authors.lastname: Antani
authors.forename: Rodney
authors.initials: R
authors.lastname: Long
mesh_terms: D000465:Algorithms; D002584:Cervix Uteri / Q000033:anatomy & histology* / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000473:pathology*