An Automatic Color Segmentation Algorithm with Application to Identification of Skin Tumor Borders

Abstract

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.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Keywords and Phrases

Biological Materials - Imaging Techniques; Color - Analysis; Computer Programming - Algorithms; Expert Systems - Knowledge Bases; Image Processing - Color Images; Automatic Color Segmentation; Skin Tumor Borders; Biological Materials; Article; Cancer Diagnosis; Color; Computer Graphics; Computer System; Dermatology; Diagnostic Imaging; Digital Computer; Image Analysis; Mathematical Analysis; Medical Technology; Priority Journal; Skin Cancer; Algorithms; Artificial Intelligence; Diagnosis, Computer-Assisted; Expert Systems; Human; Image Processing, Computer-Assisted; Melanoma; Skin Neoplasms; Support, Non-U.S. Gov't; Support, U.S. Gov't, Non-P.H.S.; Automatic Induction; Automatic Recognition; Computer Vision; Expert System; Image Processing; Image Segmentation; Skin Tumor

International Standard Serial Number (ISSN)

0895-6111; 1879-0771

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1992 Elsevier, All rights reserved.

Publication Date

01 May 1992

PubMed ID

1623498

Link to Full Text

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