TY - JOUR
T1 - Automatic detection of anatomical landmarks in uterine cervix images
AU - Greenspan, Hayit
AU - Gordon, Shiri
AU - Zimmerman, Gali
AU - Lotenberg, Shelly
AU - Jeronimo, Jose
AU - Antani, Sameer
AU - Long, Rodney
N1 - Funding 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: [email protected]).
PY - 2009/3
Y1 - 2009/3
N2 - 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.
AB - 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.
KW - Cervical cancer
KW - Curvature features
KW - Image segmentation
KW - Landmark extraction
KW - Medical image analysis
UR - http://www.scopus.com/inward/record.url?scp=61549099002&partnerID=8YFLogxK
U2 - 10.1109/TMI.2008.2007823
DO - 10.1109/TMI.2008.2007823
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AN - SCOPUS:61549099002
SN - 0278-0062
VL - 28
SP - 454
EP - 468
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 3
ER -
