Automated Spatial Pattern Analysis for Identification of Foot Arch Height From 2D Foot Prints

Frontiers in Physiology

Julien Lucas¹  Kinda Khalaf²  Jorge J. G. Leandro⁴  Herbert F. Jelinek⁵  James Charles⁶ 

[1] Department of Biology and Computer Science, University of Poitiers, Poitiers, France;Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates;Institute of Koorie Education, Deakin University, Waurn Ponds, VIC, Australia;Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil;School of Community Health, Charles Sturt University, Albury, NSW, Australia;School of Medicine, Deakin University, Waurn Ponds, VIC, Australia;

关键词: non-linear dynamics;    complexity;    wavelet analysis;    bending energy;    foot arch height;   

DOI  :  10.3389/fphys.2018.01216

来源: DOAJ

【 摘 要 】

Arch height is an important determinant for the risk of foot pathology, especially in an aging population. Current methods for analyzing footprints require substantial manual processing time. The current research investigated automated determination of foot type based on features derived from the Gabor wavelet utilizing digitized footprints to allow timely assessment of foot type and focused intervention. Two hundred and eighty footprints were collected, and area, perimeter, curvature, circularity, 2nd wavelet moment, mean bending energy (MBE), and entropy were determined using in house developed MATLAB codes. The results were compared to the gold standard using Spearman’s Correlation coefficient and multiple linear regression models with significance set at 0.05. The proposed approach found MBE combined with foot perimeter to give the best results as shown by ANOVA (F(2,211) = 10.18, p < 0.0001) with the mean ±SD of low, normal, and high arch being, respectively, 0.26 ± 0.025,.24 ± 0.021, and 0.23 ± 0.024. A clinical review of the new cut off values, as set by the first and the third quartiles of our sample, lead to reliability up to 87%. Our results suggest that automated wavelet-based foot type classification of 2D binary images of the plantar surface of the foot is comparable to current state-of-the-art methods providing a cost and time effective tool suitable for clinical diagnostics.

【 授权许可】

Unknown