Segmentation of Mosaicism in Cervicographic Images Using Support Vector Machines

Xue Z, Long LR, Antani SK, Jeronimo J, Thoma GR
Proc. SPIE 7259, Medical Imaging 2009, Lake Buena Vista, Florida, United States: Image Processing, 72594X (27 March 2009); doi: 10.1117/12.812318; http://dx.doi.org/10.1117/12.812318.

Abstract:
The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating a large digital repository of cervicographic images for the study of uterine cervix cancer prevention. One of the research goals is to automatically detect diagnostic bio-markers in these images. Reliable bio-marker segmentation in large biomedical image collections is a challenging task due to the large variation in image appearance. Methods described in this paper focus on segmenting mosaicism, which is an important vascular feature used to visually assess the degree of cervical intraepithelial neoplasia. The proposed approach uses support vector machines (SVM) trained on a ground truth dataset annotated by medical experts (which circumvents the need for vascular structure extraction). We have evaluated the performance of the proposed algorithm and experimentally demonstrated its feasibility.