Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and
cancer mortality for both men and women. However, its death rate can be dramatically reduced by
appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and
assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the
examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist's
knowledge and experience. An automated image processing system providing an objective, rapid, and
inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and
diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-Aided-Diagnosis (CAD) system for colonoscopy - ColonoCAD<sup>TM</sup>. ColonoCAD is a complex multi-sensor, multi-data and multi-algorithm image processing system, incorporating data management and visualization, video
quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction
and classification. As this is a new field in medical image processing, our hope is that this paper will
provide the framework to encourage and facilitate collaboration and discussion between industry,
academia, and medical practitioners.
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.
A 3D colon model is an essential component of a computer-aided diagnosis (CAD) system in colonoscopy to
assist surgeons in visualization, and surgical planning and training. This research is thus aimed at developing
the ability to construct a 3D colon model from endoscopic videos (or images). This paper summarizes our ongoing
research in automated model building in colonoscopy. We have developed the mathematical formulations
and algorithms for modeling static, localized 3D anatomic structures within a colon that can be rendered from
multiple novel view points for close scrutiny and precise dimensioning. This ability is useful for the scenario
when a surgeon notices some abnormal tissue growth and wants a close inspection and precise dimensioning. Our
modeling system uses only video images and follows a well-established computer-vision paradigm for image-based
modeling. We extract prominent features from images and establish their correspondences across multiple images
by continuous tracking and discrete matching. We then use these feature correspondences to infer the camera's
movement. The camera motion parameters allow us to rectify images into a standard stereo configuration and
calculate pixel movements (disparity) in these images. The inferred disparity is then used to recover 3D surface
depth. The inferred 3D depth, together with texture information recorded in images, allow us to construct a 3D
model with both structure and appearance information that can be rendered from multiple novel view points.
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.