Two color-image segmentation methods are described. The first is based on a spherical coordinate transform of original RGB data. The second is based on a mathematically optimal transform, the principal components transform (also known as eigenvector, discrete Karhunen-Loeve, or Hotelling transform). These algorithms are applied to the extraction from skin tumor images of various features such as tumor border, crust, hair scale, shiny areas, and ulcer. The results of this research will be used in the development of a computer vision system that will serve as the visual front-end of a medical expert system to automate visual feature identification for skin tumor evaluation
S. E. Umbaugh et al., "Automatic Color Segmentation Algorithms-with Application to Skin Tumor Feature Identification," IEEE Engineering in Medicine and Biology Magazine, Institute of Electrical and Electronics Engineers (IEEE), Jan 1993.
The definitive version is available at https://doi.org/10.1109/51.232346
Electrical and Computer Engineering
Keywords and Phrases
Automatic Color Segmentation Algorithms; Color-Image Segmentation Methods; Colour; Computer Vision System; Crust; Hair; Image Segmentation; Mathematically Optimal Transform; Medical Diagnostic Imaging; Medical Expert System; Medical Image Processing; Principal Components Transform; Scale; Shiny Areas; Skin; Skin Tumor Feature Identification; Spherical Coordinate Transform; Tumor Border; Ulcer
International Standard Serial Number (ISSN)
Article - Journal
© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.