TY - JOUR
T1 - Automated spatial pattern analysis for identification of foot arch height from 2D foot prints
AU - Lucas, Julien
AU - Khalaf, Kinda
AU - Charles, James
AU - Leandro, Jorge J.G.
AU - Jelinek, Herbert F.
N1 - Includes bibliographical references.
PY - 2018/9/3
Y1 - 2018/9/3
N2 - 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.
AB - 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.
KW - Bending energy
KW - Complexity
KW - Foot arch height
KW - Non-linear dynamics
KW - Wavelet analysis
UR - http://www.scopus.com/inward/record.url?scp=85053025699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053025699&partnerID=8YFLogxK
U2 - 10.3389/fphys.2018.01216
DO - 10.3389/fphys.2018.01216
M3 - Article
C2 - 30233395
AN - SCOPUS:85053025699
SN - 1664-042X
VL - 9
SP - 1
EP - 8
JO - Frontiers in Physiology
JF - Frontiers in Physiology
IS - SEP
M1 - 1216
ER -