Proceedings Article | 17 March 2006
KEYWORDS: Image quality, Cervix, Expectation maximization algorithms, RGB color model, Image analysis, Cameras, Cervical cancer, Detection and tracking algorithms, Contamination, Diagnostics
Uterine cervical cancer is the second most common cancer among women worldwide. However, its death rate can be dramatically reduced by appropriate treatment, if early detection is available. We are developing a Computer-Aided-Diagnosis (CAD) system to facilitate colposcopic examinations for cervical cancer screening and diagnosis. Unfortunately, the effort to develop fully automated cervical cancer diagnostic algorithms is hindered by the paucity of high quality, standardized imaging data. The limited quality of cervical imagery can be attributed to several factors, including: incorrect instrumental settings or positioning, glint (specular reflection), blur due to poor focus, and physical contaminants. Glint eliminates the color information in affected pixels and can therefore introduce artifacts in feature extraction algorithms. Instrumental settings that result in an inadequate dynamic range or an overly constrained region of interest can reduce or eliminate pixel information and thus make image analysis algorithms unreliable. Poor focus causes image blur with a consequent loss of texture information. In addition, a variety of physical contaminants, such as blood, can obscure the desired scene and reduce or eliminate diagnostic information from affected areas. Thus, automated feedback should be provided to the colposcopist as a means to promote corrective actions. In this paper, we describe automated image quality assessment techniques, which include region of interest detection and assessment, contrast dynamic range assessment, blur detection, and contaminant detection. We have tested these algorithms using clinical colposcopic imagery, and plan to implement these algorithms in a CAD system designed to simplify high quality data acquisition. Moreover, these algorithms may also be suitable for image quality assessment in telemedicine applications.
