title: Shape priors for segmentation of the cervix region within uterine cervix images.
abstract: The work focuses on a unique medical repository of digital uterine cervix images ("cervigrams") collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multiyear studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multistage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results.
authors.affiliation: Department of Biomedical Engineering, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel.
authors.forename: Shelly
authors.initials: S
authors.lastname: Lotenberg
authors.forename: Shiri
authors.initials: S
authors.lastname: Gordon
authors.forename: Hayit
authors.initials: H
authors.lastname: Greenspan
mesh_terms: D002451:Cell Compartmentation; D002584:Cervix Uteri / Q000473:pathology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000473:pathology*