Reframing climate change assessments around risk: recommendations for the US National Climate Assessment.

Climate change is a risk management challenge for society, with uncertain but potentially severe outcomes affecting natural and human systems, across generations. Managing climate-related risks will be more difficult without a base of knowledge and practice aimed at identifying and evaluating specific risks, and their likelihood and consequences, as well as potential actions to promote resilience in the face of these risks. We suggest three improvements to the process of conducting climate change assessments to better characterize risk and inform risk management actions.

Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location.

BACKGROUND/PURPOSE: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits. METHODS: Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue, and saturation). Color shade analysis was performed using a logistic regression model trained with an image set of 60 dermoscopic images of melanoma that contained pink areas. Detected pink shade areas were further analyzed with regard to the location within the lesion, average color parameters over the detected areas, and histogram texture features. RESULTS: Logistic regression analysis of a separate set of 128 melanomas and 128 benign images resulted in up to 87.9% accuracy in discriminating melanoma from benign lesions measured using area under the receiver operating characteristic curve. The accuracy in this model decreased when parameters for individual shades, texture, or shade location within the lesion were omitted. CONCLUSION: Texture, color, and lesion location analysis applied to multiple shades of pink can assist in melanoma detection. When any of these three details: color location, shade analysis, or texture analysis were omitted from the model, accuracy in separating melanoma from benign lesions was lowered. Separation of colors into shades and further details that enhance the characterization of these color shades are needed for optimal discrimination of melanoma from benign lesions.

Sediment filled fractures in the Permo-Triassic sandstones of the Cheshire basin: observations and implications for pollutant transport.

Fracture mapping in a tunnel system and at nearby outcrop on the Runcorn Penninsula, UK, suggests the need for a review of the potential pathways for pollutant transport in Permo-Triassic sandstone aquifers. Sediment infilling is pervasive in the largest sub-vertical multi-layer fractures in the study area, both at the surface and to a depth of about 40 m below ground level. Sediment infill is inferred to have formed in situ. The conventional models of pollutant transport in fracture networks assume that they comprise open fractures, with pollutant mobility depending on fracture connectivity (a function of density, length, orientation and intersection) and aperture. The presence of extensive sediment fills in fractures will materially change their permeability, thereby reducing pollutant flux, and be of significance in the assessment of risks arising from chemical spillages. There has been little or no substantive evidence for such fills in Permo-Triassic sandstones in the UK, apart from observations at outcrop and anecdotes of sand being pumped from boreholes. Here, we report surface and rare, but complementary, subsurface observations of extensive fills in the Cheshire basin, and argue that they will only act as preferential pathways where they crosscut low-permeability horizons such as mudstones.

Border detection on digitized skin tumor images.

A radial search technique is presented for detecting skin tumor borders in clinical dermatology images. First, it includes two rounds of radial search based on the same tumor center. The first-round search is independent, and the second-round search is knowledge-based tracking. Then a rescan with a new center is used to solve the blind-spot problem. The algorithm is tested on model images with excellent performance, and on 300 real clinical images with a satisfactory result.

Nondermatoscopic digital imaging of pigmented lesions.

BACKGROUND/AIMS: Pigmented lesions are often difficult to evaluate clinically. Improvement of diagnostic accuracy by dermatoscopy has attracted much interet. With advanced digital imaging measurement of assymmetry, border irregularity and relative color as well as texture characteristics, lesional depth and changes in lesional area are now possible, the object of this review is to conclude the present status of these techniques and their potential. CONCLUSIONS: Digital imaging of pigmented lesions to this date include acquiring and storing of images, quantification of clinical features including asymmetry, and teledermatology with transfer of images. Predicted uses include malignancy evaluation, delineation of depth of invasion and the development of large collections of pigment lesions observations. The field is rapidly expanding. As of 1994, it is unknown what role digital imaging will ultimately play in clinical dermatology.

Neural network diagnosis of malignant melanoma from color images.

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive five years [1]. Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this paper, we present a novel neural network approach for the automated separation of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics. Our approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, we are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images.

Detection of skin tumor boundaries in color images.

A simple and yet effective method for finding the borders of tumors is presented as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from the red, green, and blue planes that contains information for discriminating the tumor from the background. Using this suitable coordinate transformation, the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results that verify the effectiveness of this approach are given.

Digital imaging in dermatology.

In this article we discuss the recent surge in activity in digital imaging in dermatology. The key role of digital imaging as an adjunct to detection of early malignant melanoma, with application in following patients with the dysplastic nevus syndrome, is explored. Other current and future uses of digital imaging in image archiving, in clinical studies such as hair growth studies, and in telediagnosis are reviewed. We review the varying research activities of image analysis laboratories participating in the dermatology image researching group. Research laboratories included in this group are at Oregon Health Sciences University, Xerox Corporation, University of Arizona, University of Cincinnati, University of Munich, University of Wurzburg, University of Arkansas, Harvard University, Southern Illinois University-Edwardsville, Johns Hopkins University, National Institutes of Health, and University of Missouri at Columbia and Rolla. The role of new imaging devices in dermatology including the "nevoscope" and the dermatoscope is explored. Goals and challenges for the new technology are discussed.

Automatic detection of asymmetry in skin tumors.

Asymmetry, a critical feature in the diagnosis of malignant melanoma, is analyzed using a new algorithm to find a major axis of asymmetry and calculate the degree of asymmetry of the tumor outline. The algorithm provides a new objective definition of asymmetry. A dermatologist classified 86 tumors as symmetric or asymmetric. Borders of tumors were found either manually or automatically using a radial search method. With either method, asymmetry determination by the asymmetry algorithm agreed with the dermatologist's determination of asymmetry in about 93% of cases.

An automatic color segmentation algorithm with application to identification of skin tumor borders.

A principal components transform algorithm for automatic color segmentation of images is described. This color segmentation algorithm was used to find tumor borders in six different color spaces including the original red, green, and blue (RGB) color space of the digitized image, the intensity/hue/saturation (IHS) transform, the spherical transform, chromaticity coordinates, the CIE transform and the uniform color transform designated CIE-LUV. Five hundred skin tumor images were separated into a training set and a test set for comparison of the different color spaces. Automatic induction was applied to dynamically determine the number of colors for segmentation. Ninety-one percent of image variance was contained in the image component along the principal axis (also containing the most image information). When compared to a luminance radial search method, the principal components color segmentation border method performed equally well by one measure and 10% better by another measure, including more near border points outside the tumor. The spherical transform provides the highest success rate and the chromaticity transform the lowest error rate, although large variances in the data preclude definitive statistical comparisons.

Texture in skin images: comparison of three methods to determine smoothness.

Smooth texture, a critical feature in skin tumor diagnosis, is analyzed using three texture measurement methods. A dermatologist classified 1290 small blocks within 42 tumor images as smooth, partially smooth, or nonsmooth. Texture discriminatory power of three methods were compared: the neighboring gray-level dependence matrix (NGLDM) method of Sun and Wee, the circular symmetric autoregressive random field model of Kashyap and Khotanzad, and a new peak-variance method. The texture analysis method that allows best prediction of smoothness for our tumor domain is the NGLDM method, affording 98% correct prediction of a smooth block with 21% false positives. We discuss applicability of texture analysis to dermatology.

Automatic detection of irregular borders in melanoma and other skin tumors.

An irregularity index previously developed is applied to detect irregular borders automatically in skin tumor images, particularly malignant melanoma. The irregularity index is used to classify various tumor borders as irregular or regular. This procedure processes tumor images with borders automatically determined by a radial search algorithm previously described. Potential use of this algorithm in an in vivo skin cancer detection system and errors expected in the use of the algorithm are discussed.

Applying artificial intelligence to the identification of variegated coloring in skin tumors.

The importance of color information for the automatic diagnosis of skin tumors by computer vision is demonstrated. The utility of the relative color concept is proved by the results in identifying variegated coloring. A feature file paradigm is shown to provide an effective methodology for the independent development of software modules for expert system/computer vision research. An automatic induction tool is used effectively to generate rules for identifying variegated coloring. Variegated coloring can be identified at rates as high as 92% when using the automatic induction technique in conjunction with the color segmentation method.

Skin cancer recognition by computer vision.

Automatic detection of several features characteristic of basal cell epitheliomas is described. The features selected for this feasibility study are semitranslucency, telangiectasia, ulcer, crust, and tumor border. Image processing methods used in this study include frequency analysis of the Fourier transform of the image, the Sun-Wee texture analysis algorithm, and several other image analysis techniques suitable for skin photographs. This image analysis software is designed for use with AI/DERM, an expert system that models diagnosis of skin tumors by dermatologists.

Automatic color segmentation of images with application to detection of variegated coloring in skin tumors.

A description is given of a computer vision system, developed to serve as the front-end of a medical expert system, that automates visual feature identification for skin tumor evaluation. The general approach is to create different software modules that detect the presence or absence of critical features. Image analysis with artificial intelligence (AI) techniques, such as the use of heuristics incorporated into image processing algorithms, is the primary approach. On a broad scale, this research addressed the problem of segmentation of a digital image based on color information. The algorithm that was developed to segment the image strictly on the basis of color information was shown to be a useful aid in the identification of tumor border, ulcer, and other features of interest. As a specific application example, the method was applied to 200 digitized skin tumor images to identify the feature called variegated coloring. Extensive background information is provided, and the development of the algorithm is described.