The biology of colorectal cancer offers an opportunity for both early detection and prevention. Compared with other
imaging modalities, optical colonoscopy is the procedure of choice for simultaneous detection and removal of colonic
polyps. Computer assisted screening makes it possible to assist physicians and potentially improve the accuracy of the
diagnostic decision during the exam. This paper presents an unsupervised method to detect and track colonic lesions in
endoscopic videos. The aim of the lesion screening and tracking is to facilitate detection of polyps and abnormal mucosa
in real time as the physician is performing the procedure. For colonic lesion detection, the conventional marker
controlled watershed based segmentation is used to segment the colonic lesions, followed by an adaptive ellipse fitting
strategy to further validate the shape. For colonic lesion tracking, a mean shift tracker with background modeling is used
to track the target region from the detection phase. The approach has been tested on colonoscopy videos acquired during
regular colonoscopic procedures and demonstrated promising results.
Diagnosing cervical cancer in a woman is a multi-step procedure involving examination of the cervix, possible biopsy
and follow-up. It is open to subjective interpretation and highly dependent upon the skills of cytologists, colposcopists,
and pathologists. In an effort to reduce the subjectiveness of the colposcopist-directed biopsy and to improve the
diagnostic accuracy of colposcopy, we have developed new colposcopic imaging systems with accompanying computer
aided diagnostic (CAD) techniques to guide a colposcopist in deciding if and where to biopsy. If the biopsy's
histopathology, the identification of the disease state at the cellular and near-cellular level, is to be used as the gold
standard for CAD, then the location of the histopathologic analysis must match exactly to the location of the biopsy
tissue in the digital image. Otherwise, no matter how perfect the histopathology and the quality of the digital imagery,
the two data sets cannot be matched and the true sensitivity and specificity of the CAD cannot be ascertained. We report
here on new approaches to preserving, continuously, the location and orientation of a biopsy sample with respect to its
location in the digital image of the cervix so as to preserve the exact spatial relationship throughout the mechanical
aspects of the histopathologic analysis. This new approach will allow CAD to produce a linear diagnosis and pinpoint
the location of the tissue under examination.
Cervical intraepithelial neoplasia (CIN) exhibits certain morphologic features that can be identified during a colposcopic exam. Immature metaplastic and dysplastic cervical squamous epithelia turn white after application of acetic acid during the exam. The whitening process occurs visually over several minutes and subjectively helps to discriminate between dysplastic and normal tissue. Digital imaging technologies enable us to assist the physician in analyzing acetowhite (acetic-acid-induced) lesions in a fully automatic way. We report a study designed to measure multiple parameters of the acetowhitening process from two images captured with a digital colposcope. One image is captured before the acetic acid application, and the other is captured after the acetic acid application. The spatial change of the acetowhitening is extracted using color and texture information in the post-acetic-acid image; the temporal change is extracted from the intensity and color changes between the post-acetic-acid and pre-acetic-acid images with an automatic alignment. In particular, we propose an automatic means to calculate an opacity index that indicates the grades of temporal change. The imaging and data analysis system is evaluated with a total of 99 human subjects. The proposed opacity index demonstrates a sensitivity and specificity of 94 and 87%, respectively, for discriminating high-grade dysplasia (CIN2+) from normal and low-grade subjects, considering histology as the gold standard.
Endoscopic images suffer from a fundamental spatial distortion due to the wide angle design of the endoscope lens. This
barrel-type distortion is an obstacle for subsequent Computer Aided Diagnosis (CAD) algorithms and should be
corrected. Various methods and research models for the barrel-type distortion correction have been proposed and
studied. For industrial applications, a stable, robust method with high accuracy is required to calibrate the different types
of endoscopes in an easy of use way. The correction area shall be large enough to cover all the regions that the
physicians need to see. In this paper, we present our endoscope distortion correction procedure which includes data
acquisition, distortion center estimation, distortion coefficients calculation, and look-up table (LUT) generation. We
investigate different polynomial models used for modeling the distortion and propose a new one which provides
correction results with better visual quality. The method has been verified with four types of colonoscopes. The
correction procedure is currently being applied on human subject data and the coefficients are being utilized in a
subsequent 3D reconstruction project of colon.
Cervical Intraepithelial Neoplasia (CIN) exhibits certain morphologic features that can be identified during a visual
inspection exam. Immature and dysphasic cervical squamous epithelium turns white after application of acetic acid
during the exam. The whitening process occurs visually over several minutes and subjectively discriminates between
dysphasic and normal tissue. Digital imaging technologies allow us to assist the physician analyzing the acetic acid
induced lesions (acetowhite region) in a fully automatic way. This paper reports a study designed to measure multiple
parameters of the acetowhitening process from two images captured with a digital colposcope. One image is captured
before the acetic acid application, and the other is captured after the acetic acid application. The spatial change of the
acetowhitening is extracted using color and texture information in the post acetic acid image; the temporal change is
extracted from the intensity and color changes between the post acetic acid and pre acetic acid images with an automatic
alignment. The imaging and data analysis system has been evaluated with a total of 99 human subjects and demonstrate
its potential to screening underserved women where access to skilled colposcopists is limited.
Image analysis for automated diagnosis of cervical cancer has attained high prominence in the last decade. Automated
image analysis at all levels requires a basic segmentation of the region of interest (ROI) within a given image. The
precision of the diagnosis is often reflected by the precision in detecting the initial region of interest, especially when
some features outside the ROI mimic the ones within the same. Work described here discusses algorithms that are used
to improve the cervical region of interest as a part of automated cervical image diagnosis. A vital visual aid in diagnosing cervical cancer is the aceto-whitening of the cervix after the application of acetic acid. Color and texture are used to segment acetowhite regions within the cervical ROI. Vaginal walls along with cottonswabs
sometimes mimic these essential features leading to several false positives. Work presented here is focused towards detecting in-focus vaginal wall boundaries and then extrapolating them to exclude vaginal walls from the cervical ROI. In addition, discussed here is a marker-controlled watershed segmentation that is used to detect cottonswabs from the cervical ROI. A dataset comprising 50 high resolution images of the cervix acquired after 60 seconds of acetic acid application were used to test the algorithm. Out of the 50 images, 27 benefited from a new cervical ROI. Significant improvement in overall diagnosis was observed in these images as false positives caused by features outside the actual ROI mimicking acetowhite region were eliminated.
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.
Colposcopy is a primary diagnostic method used to detect cancer and precancerous lesions of the uterine cervix. During the examination, the metaplastic and abnormal tissues exhibit different degrees of whiteness (acetowhitening effect) after applying a 3%-5% acetic acid solution. Colposcopists evaluate the color and density of the acetowhite tissue to assess the severity of lesions for the purpose of diagnosis, telemedicine, and annotation. However, the color and
illumination of the colposcopic images vary with the light sources, the instruments and camera settings, as well as the clinical environments. This makes assessment of the color information very challenging even for an expert. In terms of developing a Computer-Aided Diagnosis (CAD) system for colposcopy, these variations affect the performance of the feature extraction algorithm for the acetowhite color. Non-uniform illumination from the light source is also an obstacle for detecting acetowhite regions, lesion margins and anatomic features. Therefore, in digital colposcopy, it is critical to map the color appearance of the images taken with different colposcopes into one standard color space with normalized
illumination. This paper presents a novel image calibration technique for colposcopic images. First, a specially designed
calibration unit is mounted on the colposcope to acquire daily calibration data prior to performing patient examinations.
The calibration routine is fast, automated, accurate and reliable. We then use our illumination correction algorithm and a color calibration algorithm to calibrate the patient data. In this paper we describe these techniques and demonstrate their applications in clinical studies.
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.