Segmentation and classification of burn images by color and texture information

Begoña Acha Pinero; Carmen Serrano; Jose Ignacio Acha; Laura M. Roa

doi:10.1117/1.1921227

1 May 2005 Segmentation and classification of burn images by color and texture information

Begoña Acha Pinero, Carmen Serrano, Jose Ignacio Acha, Laura M. Roa

Author Affiliations +

Journal of Biomedical Optics, Vol. 10, Issue 3, 034014 (May 2005). https://doi.org/10.1117/1.1921227

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%.

©(2005) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Begoña Acha Pinero, Carmen Serrano, Jose Ignacio Acha, and Laura M. Roa "Segmentation and classification of burn images by color and texture information," Journal of Biomedical Optics 10(3), 034014 (1 May 2005). https://doi.org/10.1117/1.1921227

Published: 1 May 2005

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KEYWORDS

Image segmentation

Skin

Photography

Cameras

Digital photography

Image classification

Neural networks

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