Automated image analysis of uterine cervical images

Wenjing Li; Jia Gu; Daron Ferris; Allen Poirson

doi:10.1117/12.708710

30 March 2007 Automated image analysis of uterine cervical images

Wenjing Li, Jia Gu, Daron Ferris, Allen Poirson

Author Affiliations +

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65142P (2007) https://doi.org/10.1117/12.708710
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

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) system. 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.

Citation Download Citation

Wenjing Li, Jia Gu, Daron Ferris, and Allen Poirson "Automated image analysis of uterine cervical images", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142P (30 March 2007); https://doi.org/10.1117/12.708710

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