Segmentation and classification of burn images by color and texture information.
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%.
Acha
Begoña
B
Area de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingenieros, University of Seville, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain. bacha@us.es
Serrano
Carmen
C
Acha
José I
JI
Roa
Laura M
LM
D000465:Algorithms; D001185:Artificial Intelligence; D002056:Burns / Q000145:classification* / Q000473:pathology*; D003116:Color; D003124:Colorimetry / Q000379:methods*; D005240:Feasibility Studies; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index