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Segmentation and classification of burn images by color and texture information


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Opened Access Segmentation and classification of burn images by color and texture information

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Author: Acha Piñero, Begoña
Serrano Gotarredona, María del Carmen
Acha Catalina, José Ignacio
Roa Romero, Laura María
Department: Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones
Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
Date: 2005
Published in: Journal of biomedical optics, 10 (3), 034014-1-034014-11.
Document type: Article
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%
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DOI: 10.1117/1.1921227

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