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.
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.
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.
Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.
Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method, whereby a physician (colposcopist) visually inspects the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features. Lesion characteristics such as margin; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by colposcopists to derive a clinical diagnosis. Clinicians and academia have suggested and shown proof of concept that automated image analysis of cervical imagery can be used for cervical cancer screening and diagnosis, having the potential to have a direct impact on improving women’s health care and reducing associated costs. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD. At the heart of ColpoCAD is a complex multi-sensor, multi-data and multi-feature image analysis system. A functional description is presented of the envisioned ColpoCAD system, broken down into: Modality Data Management System, Image Enhancement, Feature Extraction, Reference Database, and Diagnosis and directed Biopsies. The system design and development process of the image analysis system is outlined. The system design provides a modular and open architecture built on feature based processing. The core feature set includes the visual features used by colposcopists. This feature set can be extended to include new features introduced by new instrument technologies, like fluorescence and impedance, and any other plausible feature that can be extracted from the cervical data. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown. As this is a new and very complex field in medical image processing, the hope is that this paper can provide a framework and basis to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.
The significance and need for expert interpretation of cervigrams (images of the cervix) in the study of the uterine cervix changes and pre-neoplasic lesions preceding cervical cancer are being investigated. The National Cancer Institute has collected a unique dataset taken from patients with normal cervixes and at various stages of cervical pre-cancer and cancer. This dataset allows us the opportunity for studying the uterine cervix changes for validating the potential of automated classification and recognition algorithms in discriminating cervical neoplasia and normal tissue. Pilot studies have been designed (1) to evaluate the effect of image transformation and optimal color mapping on the accepted levels of compression needed for effective dissemination of cervical image data over a network and (2) for automated detection of lesions from feature extraction, registration, and segmentation of lesions in cervix image sequences. In this paper, we present the results of the effectiveness of a novel, wavelet based, multi-spectral analyzer in retaining diagnostic features in encoded cervical images, thus allowing investigation on the potential of automated detection of lesions in cervix image sequences using automated registration, color transformation and bit-rate control, and a statistical segmentation approach.
Cervical cancer is the second most common malignancy in women worldwide. If diagnosed in the premalignant stage, cure is invariably assured. Although the Papanicolaou (Pap) smear has significantly reduced the incidence of cervical cancer where implemented, the test is only moderately sensitive, highly subjective and skilled-labor intensive. Newer optical screening tests (cervicography, direct visual inspection and speculoscopy), including fluorescent and reflective spectroscopy, are fraught with certain weaknesses. Yet, the integration of optical probes for the detection and discrimination of cervical neoplasia with automated image analysis methods may provide an effective screening tool for early detection of cervical cancer, particularly in resource poor nations. Investigative studies are needed to validate the potential for automated classification and recognition algorithms. By applying image analysis techniques for registration, segmentation, pattern recognition, and classification, cervical neoplasia may be reliably discriminated from normal epithelium. The National Cancer Institute (NCI), in cooperation with the National Library of Medicine (NLM), has embarked on a program to begin this and other similar investigative studies.
Optical spectroscopy has been shown to be an effective method for detecting neoplasia of epithelial tissues. Most studies to date in this realm have applied fluorescence or reflectance spectroscopy alone as a preferred method of disease detection. We have been developing instrumentation which can acquire both reflectance and fluorescence images of the human cervix in vivo, with the goal of combining multispectral information from the two spectroscopic modalities. This instrumentation has been tested on a group of patients in a clinical setting. We have applied spectral and spatial analysis techniques to the acquired images to assess the capabilities of this technology to discriminate neoplastic from normal cervical tissue.