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.

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.

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.

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.