Investigating CBIR Techniques for Cervicographic Images

Xue Z, Antani S, Long LR, Jeronimo J, Thoma GR
AMIA Annu Symp Proc. 2007 Oct 11:826-30.

The National Library of Medicine (NLM) and the National Cancer Institute (NCI) are creating a digital archive of 100,000 cervicographic images and clinical and diagnostic data obtained through two major longitudinal studies. In addition to developing tools for Web access to these data, we are conducting research in Content-Based Image Retrieval (CBIR) techniques for retrieving visually similar and pathologically relevant images. The resulting system of tools is expected to greatly benefit medical education and research into uterine cervical cancer which is the second most common cancer affecting women worldwide. Our current prototype system with fundamental CBIR functions operates on a small test subset of images and retrieves relevant cervix images containing tissue regions similar in color, texture, size, and/or location to a query image region marked by the user. Initial average precision result for retrieval by color of acetowhite lesions is 52%, and for the columnar epithelium is 64.2%, respectively.

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