Title :
Automatic Detection of Anatomical Landmarks in Uterine Cervix Images
Author :
Greenspan, Hayit ; Gordon, Shiri ; Zimmerman, Gali ; Lotenberg, Shelly ; Jeronimo, Jose ; Antani, Sameer ; Long, Rodney
Author_Institution :
Dept. of Biomed. Eng., Tel-Aviv Univ., Ramat-Aviv
fDate :
3/1/2009 12:00:00 AM
Abstract :
The work focuses on a unique medical repository of digital cervicographic images (ldquoCervigramsrdquo) 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 ldquoosrdquo), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.
Keywords :
biological organs; cancer; edge detection; feature extraction; gynaecology; image segmentation; medical image processing; tumours; NCI; NLM; National Cancer Institute; National Library of Medicine; Web-accessible database; automatic anatomical landmark detection; cervical cancer; cervigrams; cervix boundary detection; cervix region extraction; digital cervicographic images; endocervical canal; image preprocessing step; image segmentation; lesion evolution; regions-of-anatomical interest labeling; specular reflection; uterine cervix images; Biomedical engineering; Biomedical imaging; Cancer detection; Cervical cancer; Image databases; Image segmentation; Lesions; Medical diagnostic imaging; Medical treatment; Software libraries; Cervical cancer; curvature features; image segmentation; landmark extraction; medical image analysis; Algorithms; Cervix Uteri; Female; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Information Storage and Retrieval; Normal Distribution; Pattern Recognition, Automated; Sensitivity and Specificity; Uterine Cervical Neoplasms;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2008.2007823
