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Proceedings Paper

Segmentation of burn images using the L*u*v* space and classification of their depths by color and texture imformation
Author(s): Begona Acha Pinero; Carmen Serrano; Jose Ignacio Acha
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Paper Abstract

In this paper a burn color image segmentation and classification algorithm is proposed. The aim of the algorithm is to separate the burn wounds from healthy skin, and the different types of burns (burn depths) among themselves. We use digital color photographs. The system is based on the color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. We use a perceptually uniform color space (L*u*v*), since Euclidean distances calculated in this space correspond to perceptually color differences. After the burn is segmented, some color and texture descriptors features are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into three types of burns: superficial dermal, depth dermal and full thickness. We get an average classification success rate of 88.89%.

Paper Details

Date Published: 9 May 2002
PDF: 8 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467117
Show Author Affiliations
Begona Acha Pinero, Univ. of Seville (Spain)
Carmen Serrano, Univ. of Seville (Spain)
Jose Ignacio Acha, Univ. of Seville (Spain)


Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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