Boundary Detection in Skin Tumor Images: An Overall Approach and a Radial Search Algorithm
Abstract
Although computerized boundary detection has been studied in depth, current general algorithms are not highly successful when applied to in vivo medical images where the borders are often not clearly defined and are sometimes difficult even for the human eye to detect. In these cases, domain-specific algorithms are necessary to achieve the required accuracy. This paper addresses the problem of automatic detection of tumor borders in digitized images of skin tumors. The complexity of boundary detection in this domain is such that algorithms based on single boundary determinants such as color, luminance, texture, or three-dimensional information are not capable of correctly identifying the boundary in all cases. Hence, an overall approach is discussed that will use confidence levels to combine results from several border detection algorithms based on the individual criteria mentioned. The development of one such algorithm (using a radial search method on luminance information) and its results are presented. With this approach, problems such as numerous false borders and unknown shape and size of the tumors are overcome.
Recommended Citation
J. E. Golston et al., "Boundary Detection in Skin Tumor Images: An Overall Approach and a Radial Search Algorithm," Pattern Recognition, vol. 23, no. 11, pp. 1235 - 1247, Elsevier, Jan 1990.
The definitive version is available at https://doi.org/10.1016/0031-3203(90)90119-6
Department(s)
Electrical and Computer Engineering
Second Department
Chemistry
Sponsor(s)
National Science Foundation (U.S.). Small Business Innovation Research Program
Keywords and Phrases
Computer Programming - Algorithms; Computer Vision - Medical Applications; Pattern Recognition - Medical Applications; Boundary Detection; Edge Detection; Melanoma; Radial Search Algorithm; Skin Cancer; Skin Tumor Images; Biomedical Engineering; Computer Vision; Luminance; Radial Search
International Standard Serial Number (ISSN)
0031-3203
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 1990 Elsevier, All rights reserved.
Publication Date
01 Jan 1990
Comments
This material is based in part upon work supported by the National Science Foundation under SBIR Phase II award number ISI-8521284.