Automatic detection of anatomical landmarks in uterine cervix images

Hayit Greenspan, Shiri Gordon, Gali Zimmerman, Shelly Lotenberg, Jose Jeronimo, Sameer Antani, Rodney Long

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

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.

Original languageEnglish
Pages (from-to)454-468
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume28
Issue number3
DOIs
StatePublished - 1 Mar 2009
Externally publishedYes

Keywords

  • Cervical cancer
  • Curvature features
  • Image segmentation
  • Landmark extraction
  • Medical image analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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