Automatic Color Segmentation Algorithms-with Application to Skin Tumor Feature Identification

Scott E. Umbaugh
Randy Hays Moss, Missouri University of Science and Technology
William V. Stoecker, Missouri University of Science and Technology
G. A. Hance

This document has been relocated to

There were 35 downloads as of 27 Jun 2016.


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