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Journal of Biomedical Optics

Segmentation and classification of burn images by color and texture information
Author(s): Begoña Acha Pinero; Carmen Serrano; Jose Ignacio Acha; Laura M. Roa
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Paper Abstract

In this paper, a burn color image segmentation and classification system is proposed. The aim of the system is to separate burn wounds from healthy skin, and to distinguish among the different types of burns (burn depths). Digital color photographs are used as inputs to the system. The system is based on color and texture information, since these are the characteristics observed by physicians in order to form a diagnosis. A perceptually uniform color space (L*u*v*) was used, since Euclidean distances calculated in this space correspond to perceptual color differences. After the burn is segmented, a set of color and texture features is calculated that serves as the input to a Fuzzy-ARTMAP neural network. The neural network classifies burns into three types of burn depths: superficial dermal, deep dermal, and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images, yielding an average classification success rate of 82%.

Paper Details

Date Published: 1 May 2005
PDF: 11 pages
J. Biomed. Opt. 10(3) 034014 doi: 10.1117/1.1921227
Published in: Journal of Biomedical Optics Volume 10, Issue 3
Show Author Affiliations
Begoña Acha Pinero, Univ. de Sevilla (Spain)
Carmen Serrano, Univ. de Sevilla (Spain)
Jose Ignacio Acha, Univ. de Sevilla (Spain)
Laura M. Roa, Univ. de Sevilla (Spain)

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