Title |
Wound perimeter, area, and volume measurement based on laser 3D and color acquisition
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Published in |
BioMedical Engineering OnLine, April 2015
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DOI | 10.1186/s12938-015-0031-7 |
Pubmed ID | |
Authors |
Urban Pavlovčič, Janez Diaci, Janez Možina, Matija Jezeršek |
Abstract |
Wound measuring serves medical personnel as a tool to assess the effectiveness of a therapy and predict its outcome. Clinically used methods vary from measuring using rules and calipers to sophisticated methods, based on 3D measuring. Our method combines the added value of 3D measuring and well-known segmentation algorithms to enable volume calculation and achieve reliable and operator-independent analysis, as we demonstrate in the paper. Developed 3D measuring system is based on laser triangulation with simultaneous color acquisition. Wound shape analysis is based on the edge-determination, virtual healthy skin approximation over the wound and perimeter, area, and volume calculation. In order to validate the approach, eight operators analyzed four different wounds using proposed method. Measuring bias was assessed by comparing measured values with expected values on an artificially modeled set of wounds. Results indicate that the perimeter, area, and volume are measured with a repeatability of 2.5 mm, 12 mm(2), and 30 mm(3), respectively, and with a measuring bias of -0.2 mm, -8.6 mm(2), 24 mm(3), respectively. According to the results of verification and the fact that typical wound analysis takes 20 seconds, the method for wound shape measurement can be clinically used as a precise tool for objectively monitoring the wound healing based on measuring its 3D shape and color. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 1% |
Unknown | 72 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 12 | 16% |
Student > Master | 8 | 11% |
Student > Doctoral Student | 8 | 11% |
Student > Ph. D. Student | 7 | 10% |
Other | 6 | 8% |
Other | 16 | 22% |
Unknown | 16 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 15 | 21% |
Computer Science | 10 | 14% |
Nursing and Health Professions | 7 | 10% |
Engineering | 7 | 10% |
Social Sciences | 2 | 3% |
Other | 13 | 18% |
Unknown | 19 | 26% |