Segmentation and automated measurement of chronic wound images: probability map approach

Mohammad Faizal Ahmad Fauzi; Ibrahim Khansa; Karen Catignani; Gayle Gordillo; Chandan K. Sen; Metin N. Gurcan

doi:10.1117/12.2043791

24 March 2014 Segmentation and automated measurement of chronic wound images: probability map approach

Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle Gordillo, Chandan K. Sen, Metin N. Gurcan

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903507 (2014) https://doi.org/10.1117/12.2043791
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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%.

Citation Download Citation

Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle Gordillo, Chandan K. Sen, and Metin N. Gurcan "Segmentation and automated measurement of chronic wound images: probability map approach", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903507 (24 March 2014); https://doi.org/10.1117/12.2043791

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available