Continuous monitoring of changes in wound size is key to correctly predict whether wounds will heal readily with conventional treatment or require more aggressive treatment strategies. Unfortunately, existing wound measurement solutions don’t meet the clinical demand due to their limitations in accuracy, operating complexity and time, acquisition and operation cost, or reproducibility, resulting in unnecessarily lengthy recovery or extra treatment procedures, incurring an excessively high financial cost, and in many cases extended usage of addictive painkillers. In this thesis, we proposed and developed a low cost, a portable non-contact solution that combines multi-spectral imaging and a portfolio of imaging processing technologies to enable automatic and instantaneous wound identification and measurements. It provides full measurements of a wound: surface area, perimeter, length, and width, without requiring the calibration process as other existing photogrammetry or laser solutions. We have developed a prototype system that illustrates our image and wound analysis capabilities using off-shelf sensor units for capturing images. Our system is capable of identifying emulated wounds in any part of human body surface automatically and highlights them on a customized GUI instantly. Image processing engine running in background analyze and computes wound dimensions with an accuracy of 95%. Our experiment results indicated that the system is reliable, consistent, accurate and reproducible.
This research has recently been selected to the 2017 I-Corps@Ohio program, a statewide program to assist faculty and graduate students from Ohio universities and colleges in validating the market potential of their technologies and assisting with launching startup companies.