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Proceedings Paper

Wound image analysis system for diabetics
Author(s): Lei Wang; Peder C. Pedersen; Diane Strong; Bengisu Tulu; Emmanuel Agu
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Paper Abstract

Diabetic foot ulcers represent a significant health issue, and daily wound care is necessary for wound healing to occur. The goal of this research is to create a smart phone based wound image analysis system for people with diabetes to track the healing process of chronic ulcers and wounds. This system has been implemented on an Android smart phone in collaboration with a PC (or embedded PC). The wound image is captured by the smart phone camera and transmitted to the PC via Wi-Fi for image processing. The PC converts the JPEG image to bitmap format, then performs boundary segmentation on the wound in the image. The segmentation is done with a particular implementation of the level set algorithm, the distance regularized level set evolution (DRLSE) method, which eliminates the need for re-initialization of level set function. Next, an assessment of the wound healing is performed with color segmentation within the boundaries of the wound image, by applying the K-Mean color clustering algorithm based on the red-yellow-black (RYB) evaluation model. Finally, the results are re-formatted to JPEG, transmitted back to the smart phone and displayed. To accelerate the wound image segmentation, we have implemented the DRLSE method on the GPU and CPU cooperative hardware platform in data-parallel mode, which has greatly improved the computational efficiency. Processing wound images acquired from UMASS Medical Center has demonstrated that the wound image analysis system provides accurate wounds area determination and color segmentation. For all wound images of size around 640 x 480, with complicated wound boundaries, the wound analysis consumed max 3s, which is 5 times faster than the same algorithm running on the CPU alone.

Paper Details

Date Published: 13 March 2013
PDF: 14 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866924 (13 March 2013); doi: 10.1117/12.2004762
Show Author Affiliations
Lei Wang, Worcester Polytechnic Institute (United States)
Peder C. Pedersen, Worcester Polytechnic Institute (United States)
Diane Strong, Worcester Polytechnic Institute (United States)
Bengisu Tulu, Worcester Polytechnic Institute (United States)
Emmanuel Agu, Worcester Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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