A new total body scanning system for automatic change detection in multiple pigmented skin lesions (original) (raw)
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Melanoma screening with serial whole body photographic change detection using Melanoscan® technology
Dermatology Online Journal, 2009
The use of an automated, whole-body, diffusely lit digital imaging enclosure to produce serial images, which were then compared, using an astrophysics image display method, enabled a private practice dermatologist to detect melanoma at significantly thinner Breslow depths compared to all other clinical detection paradigms examined in this study. The patients were triaged to scanning using a melanoma risk survey system. The system employed a 24 camera semicircular imaging wall, with front and back views. 10,000 whole body photographic scans were obtained. Privacy was maintained with 128-bit image encryption and off-line storage. Image to image comparison of whole body digital photography was combined with a whole body skin exam in order to sensitize a clinical dermatologist to skin changes in individuals at risk for melanoma. Mean depths (Breslow scores) were compiled from six distinct melanoma biopsy cohorts segregated and based on different clinical screening paradigms. The Breslow depth of invasive lesions of the serial screening cohort was significantly less (by at least 0.050 mm) compared to three other clinical screening groups (patient self-detection 0.55 mm, p=0.007; referred by outside nondermatologist physician 0.73 mm, p=0.03; and serial dermatologic evaluation 0.23 mm, p=0.03) as well as two pathology laboratory cohorts (community hospital laboratory 1.45 mm, p=0.003; dermatopathology laboratory 0.18, p=0.0003). This approach provides a quick and effective method for detection of early melanomas with a significant reduction in the skin area required for lesion examination.
Total body photography for skin cancer screening
International Journal of Dermatology, 2014
Background-Total body photography may aid in melanoma screening, but is not widely applied due to time and cost. We hypothesized that a near-simultaneous automated skin photoacquisition system would be acceptable to patients and could rapidly obtain total body photographic images that enable visualization of pigmented skin lesions. Methods-From 2/09-5/09, a study of 20 volunteers was performed at the University of Virginia to test a prototype 16-camera imaging booth built by the research team and to guide development of special purpose software. For each participant, images were obtained before and after marking ten lesions (5 "easy" and 5 "difficult"), and images were evaluated to estimate visualization rates. Imaging logistical challenges were scored by the operator, and participant opinion was assessed by questionnaire. Results-Average time for image capture was 3 minutes (range 2-5). All 55 "easy" lesions were visualized (sensitivity 100%, 90%CI 95-100%) and 54/55 "difficult" lesions were visualized (sensitivity 98%, 90%CI 92-100%). Operators and patients graded the imaging process favorably, with challenges identified regarding lighting and positioning. Conclusions-Rapid-acquisition automated skin photography is feasible with a low-cost system, with excellent lesion visualization and participant acceptance. These data provide a basis for employing this method in clinical melanoma screening.
Optical detection and monitoring of pigmented skin lesions
Biomedical Optics Express, 2017
A method is presented for discriminating between malignant and benign pigmented skin lesions based on multispectral and multi-angle images. It is discussed how to retrieve maps of physiology properties and morphometric parameters from recorded images using a biooptical model, radiative transfer calculations, and nonlinear inversion, and how to employ automated zooming to extract lesion and surrounding masks. Training and validation of a classification scheme for separation between benign and malignant tissue yielded sensitivity/specificity ranging from 97%/97% for application to a small dataset comprised of lesions not used for training and validation to 99%/93% for application to a larger dataset.
IEEE Journal of Selected Topics in Signal Processing, 2000
In this paper, we describe an automatic system for inspection of pigmented skin lesions and melanoma diagnosis, which supports images of skin lesions acquired using a conventional (consumer level) digital camera. More importantly, our system includes a decision support component, which combines the outcome of the image classification with context knowledge such as skin type, age, gender, and affected body part. This allows the estimation of the personal risk of melanoma, so as to add confidence to the classification. We found that our system classified images with an accuracy of 86%, with a sensitivity of 94%, and specificity of 68%. The addition of context knowledge was indeed able to point to images that were erroneously classified as benign, albeit not to all of them.
Automated prescreening of pigmented skin lesions using standard cameras
Computerized Medical Imaging and Graphics, 2011
This paper describes a new method for classifying pigmented skin lesions as benign or malignant. The skin lesion images are acquired with standard cameras, and our method can be used in telemedicine by non-specialists. Each acquired image undergoes a sequence of processing steps, namely: (1) preprocessing, where shading effects are attenuated; (2) segmentation, where a 3-channel image representation is generated and later used to distinguish between lesion and healthy skin areas; (3) feature extraction, where a quantitative representation for the lesion area is generated; and (4) lesion classification, producing an estimate if the lesion is benign or malignant (melanoma). Our method was tested on two publicly available datasets of pigmented skin lesion images. The preliminary experimental results are promising, and suggest that our method can achieve a classification accuracy of 96.71%, which is significantly better than the accuracy of comparable methods available in the literature.
Journal of Medical Systems, 2015
In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.
Computer-assisted detection of subcutaneous melanomas
Academic Radiology, 2004
Rationale and Objectives. Subcutaneous melanomas may be missed on computed tomography because of their peripheral location or perceived unimportance, yet they can have clinical significance. The use of a novel computer-assisted detection scheme to locate subcutaneous melanoma lesions in body CT images was investigated. Materials and Methods. The detection software segments subcutaneous fat from the rest of the body and searches for soft tissue density lesions that match a size and shape constraint. Sensitivity and specificity of the proposed method was analyzed by comparing automated lesion detection results in eight patients with 118 subcutaneous melanomas with ground truth data derived from manual tracings of a trained observer. Results. The sensitivity of subcutaneous melanoma detection was 86%. The false-positive rate was 3.1 per slice. Analysis of the false-positives showed that the most common cause was incorrect classification of muscle as a nodule. Conclusion. This study showed the feasibility of a fully automatic subcutaneous melanoma lesion detection system having good sensitivity. The false-positive rate was high, but avenues for further reduction were identified.