Greenspan Hayit, Gordon Shiri, Zimmerman Gali, Lotenberg Shelly, Jeronimo Jose, Antani Sameer, Long Rodney
Department of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Ramat-Aviv 69978, Israel.
IEEE Trans Med Imaging. 2009 Mar;28(3):454-68. doi: 10.1109/TMI.2008.2007823.
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.
这项工作聚焦于美国国立癌症研究所（NCI）在多年纵向研究中收集的独特的数字宫颈造影图像（“宫颈图像”）医学库。NCI与美国国立医学图书馆（NLM）合作，正在开发一个独特的可通过网络访问的数字化宫颈图像数据库，以研究与宫颈癌相关病变的演变。需要工具对宫颈图像内容进行自动分析，以支持癌症研究。我们提出了一种多阶段方案，用于分割和标记宫颈图像中感兴趣的解剖区域。特别地，我们专注于宫颈区域的提取和宫颈边界的精确检测；消除镜面反射作为重要的预处理步骤；此外，检测子宫颈管入口（“宫颈口”）。在由NCI专家手动标注的三个宫颈图像集上评估分割结果。
