Automated image analysis of uterine cervical images

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.

[1]  Wenjing Li,et al.  Detection and Characterization of Abnormal Vascular Patterns in Automated Cervical Image Analysis , 2006, ISVC.

[2]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[3]  Jia Gu,et al.  Computer-Aided Diagnosis (CAD) for Cervical Cancer Screening and Diagnosis: A New System Design in Medical Image Processing , 2005, CVBIA.

[4]  Paul Scheunders,et al.  Wavelet correlation signatures for color texture characterization , 1999, Pattern Recognit..

[5]  Holger Lange,et al.  Automatic glare removal in reflectance imagery of the uterine cervix , 2005, SPIE Medical Imaging.

[6]  Viara Van Raad.  Active contour models - a multiscale implementation for anatomical feature delineation in cervical images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[7]  Shiri Gordon,et al.  Content analysis of uterine cervix images: initial steps toward content based indexing and retrieval of cervigrams , 2006, SPIE Medical Imaging.

[8]  Hayit Greenspan,et al.  Automatic detection of specular reflections in uterine cervix images , 2006, SPIE Medical Imaging.

[9]  Ulf Gustafsson,et al.  A new image calibration system in digital colposcopy. , 2006, Optics express.

[10]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jose Jeronimo,et al.  A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer , 2004, SPIE Medical Imaging.

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Shiri Gordon,et al.  Image segmentation of uterine cervix images for indexing in PACS , 2004 .