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Improving Dermoscopy Image Classification Using Color Constancy | IEEE Journals & Magazine | IEEE Xplore

Improving Dermoscopy Image Classification Using Color Constancy


Abstract:

Robustness is one of the most important characteristics of computer-aided diagnosis systems designed for dermoscopy images. However, it is difficult to ensure this charac...Show More

Abstract:

Robustness is one of the most important characteristics of computer-aided diagnosis systems designed for dermoscopy images. However, it is difficult to ensure this characteristic if the systems operate with multisource images acquired under different setups. Changes in the illumination and acquisition devices alter the color of images and often reduce the performance of the systems. Thus, it is important to normalize the colors of dermoscopy images before training and testing any system. In this paper, we investigate four color constancy algorithms: Gray World, max-RGB, Shades of Gray, and General Gray World. Our results show that color constancy improves the classification of multisource images, increasing the sensitivity of a bag-of-features system from 71.0% to 79.7% and the specificity from 55.2% to 76% using only 1-D RGB histograms as features.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 19, Issue: 3, May 2015)
Page(s): 1146 - 1152
Date of Publication: 25 July 2014

ISSN Information:

PubMed ID: 25073179

Funding Agency:


References

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