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Proceedings Paper

Segmentation and automated measurement of chronic wound images: probability map approach
Author(s): Mohammad Faizal Ahmad Fauzi; Ibrahim Khansa; Karen Catignani; Gayle Gordillo; Chandan K. Sen; Metin N. Gurcan
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Paper Abstract

estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue composition, measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map; 3) Auto-calibration of measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.

Paper Details

Date Published: 24 March 2014
PDF: 8 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903507 (24 March 2014); doi: 10.1117/12.2043791
Show Author Affiliations
Mohammad Faizal Ahmad Fauzi, The Ohio State Univ. (United States)
Multimedia Univ. (Malaysia)
Ibrahim Khansa, The Ohio State Univ. (United States)
Karen Catignani, The Ohio State Univ. (United States)
Gayle Gordillo, The Ohio State Univ. (United States)
Chandan K. Sen, The Ohio State Univ. Wexner Medical Ctr. (United States)
Metin N. Gurcan, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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