Cervical Cancer is the second most common cancer among women worldwide and the leading cause of cancer mortality
of women in developing countries. If detected early and treated adequately, cervical cancer can be virtually prevented.
Cervical precursor lesions and invasive cancer exhibit certain morphologic features that can be identified during a visual
inspection exam. Digital imaging technologies allow us to assist the physician with a Computer-Aided Diagnosis (CAD)
In colposcopy, epithelium that turns white after application of acetic acid is called acetowhite epithelium. Acetowhite
epithelium is one of the major diagnostic features observed in detecting cancer and pre-cancerous regions. Automatic
extraction of acetowhite regions from cervical images has been a challenging task due to specular reflection, various
illumination conditions, and most importantly, large intra-patient variation. This paper presents a multi-step acetowhite
region detection system to analyze the acetowhite lesions in cervical images automatically. First, the system calibrates
the color of the cervical images to be independent of screening devices. Second, the anatomy of the uterine cervix is
analyzed in terms of cervix region, external os region, columnar region, and squamous region. Third, the squamous
region is further analyzed and subregions based on three levels of acetowhite are identified. The extracted acetowhite
regions are accompanied by color scores to indicate the different levels of acetowhite. The system has been evaluated by
40 human subjects' data and demonstrates high correlation with experts' annotations.