27 February 2018 Asymmetry quantification from reflectance images of orthotic patients using structural similarity metrics
Author Affiliations +
Pathologies like plantar fasciitis, a common soft tissue disorder of the foot, is frequently associated with older age, high BMI and little exercise. Like other pathologies associated with the foot, the knee or hip, foot orthoses can help the patient’s posture and recent techniques allow the creation of personalized foot orthoses based on 3D foot model that are fitted with high accuracy to the foot surface. In order to assess the efficacy of the personalized orthoses on the patient’s pose and balance, depth images with reflectance camera filters are acquired in order to evaluate the posture of the patient before and after the use of the orthoses. Images are analysed by clinicians to assess the region asymmetry and posture changes. However, this remains a subjective evaluation and a quantifiable measurement is required to follow patient progression. In this paper, we present a novel tool to assess and quantify the asymmetry of body regions using a color-based structural similarity metric calculated from paired regions. This provides a quantitative measure to evaluate the effect of the personalized orthoses on the patient. A user-friendly interface allows the user to select an area of the body and automatically generate a symmetry axis, along with a measure of asymmetry measuring reflectance variations from the skin. The tool was validated on 30 patients, demonstrating an 83% agreement rate compare to clinical observations.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marc-Antoine Boucher, Marc-Antoine Boucher, Nicolas Watts, Nicolas Watts, Frederic Gremillet, Frederic Gremillet, Philippe Legare, Philippe Legare, Samuel Kadoury, Samuel Kadoury, } "Asymmetry quantification from reflectance images of orthotic patients using structural similarity metrics", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753G (27 February 2018); doi: 10.1117/12.2292544; https://doi.org/10.1117/12.2292544

Back to Top