The images used in this research were digitized from 35mm color photographic slides obtained from a private dermatology practice and from New York University. The authors compared 6 color segmentation methods and their effectiveness as part of an overall border-finding algorithm. The PCT/median cut and adaptive thresholding algorithms provided the lowest average error and show the most promise for further individual algorithm development. Combining the different methods resulted in further improvement in the number of correctly identified tumor borders, and by incorporating additional heuristics in merging the segmented object information, one could potentially further increase the success rate. The algorithm is broad-based and suggests several areas for further research. One possible area of exploration is to incorporate an intelligent decision making process as to the number of colors that should be used for segmentation in the PCT/median cut and adaptive thresholding algorithms. For comparison purposes, the number of colors was kept constant at three in the authors'' application. Other areas that can be explored are noise removal and object classification to determine the correct tumor object
R. H. Moss et al., "Unsupervised Color Image Segmentation: with Application to Skin Tumor Borders," IEEE Engineering in Medicine and Biology Magazine, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.
The definitive version is available at https://doi.org/10.1109/51.482850
Electrical and Computer Engineering
Keywords and Phrases
35 Mm; 35 Mm Color Photographic Slides; New York University; PCT/Median Cut Algorithm; Adaptive Thresholding Algorithm; Color Segmentation Methods; Correct Tumor Object; Edge Detection; Heuristics; Image Segmentation; Individual Algorithm Development; Information Merging; Intelligent Decision Making Process; Medical Diagnostic Imaging; Medical Image Processing; Noise Removal; Object Classification; Photographic Applications; Private Dermatology Practice; Segmented Object Information; Skin; Skin Tumor Borders; Unsupervised Color Image Segmentation
International Standard Serial Number (ISSN)
Article - Journal
© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 1996