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.
S. E. Umbaugh et al., "Automatic Color Segmentation of Images with Application to Detection of Variegated Coloring in Skin Tumors," IEEE Engineering in Medicine and Biology Magazine, Institute of Electrical and Electronics Engineers (IEEE), Jan 1989.
The definitive version is available at https://doi.org/10.1109/51.45955
Electrical and Computer Engineering
Keywords and Phrases
Artificial Intelligence Techniques; Automatic Color Segmentation; Color; Computer Vision; Computer Vision System; Critical Features; Digital Image Segmentation; Expert Systems; Image Processing Algorithms; Medical Diagnostic Computing; Medical Expert System; Skin; Skin Tumors; Software Modules; Ulcer; Variegated Coloring Detection; Visual Feature Identification
International Standard Serial Number (ISSN)
Article - Journal
© 1989 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.