IEEE J Biomed Health Inform - Improving dermoscopy image classification using color constancy.

Tópicos

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Resumo

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.

Resumo Limpo

robust one import characterist computeraid diagnosi system design dermoscopi imag howev difficult ensur characterist system oper multisourc imag acquir differ setup chang illumin acquisit devic alter color imag often reduc perform system thus import normal color dermoscopi imag train test system paper investig four color constanc algorithm gray world maxrgb shade gray general gray world result show color constanc improv classif multisourc imag increas sensit bagoffeatur system specif use d rgb histogram featur

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