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Digital image processing technique to measure the range of motion of the elbow Cover

Digital image processing technique to measure the range of motion of the elbow

Asian Biomedicine
Volume 14 (2020): Issue 1 (February 2020)
By: Chris Charoenlap and  Krerk Piromsopa  
Open Access
|Jul 2020

Figures & Tables

Figure 1

Photographic positions. Subject laterally abducted shoulder perpendicular to the floor and fully extended elbow (A). Elbow was maximally flexed (B).
Photographic positions. Subject laterally abducted shoulder perpendicular to the floor and fully extended elbow (A). Elbow was maximally flexed (B).

Figure 2

Line detection process. (A) Cropped image. (B) Converting red, green, and blue (RGB) to hue saturation value (HSV). (C) Detection of lines using Canny and Hough transformation.
Line detection process. (A) Cropped image. (B) Converting red, green, and blue (RGB) to hue saturation value (HSV). (C) Detection of lines using Canny and Hough transformation.

Figure 3

Angle calculation step for extended elbow image. (A): Locate middle points of arm and forearm from one-third of distance of both edges. (B): Calculate angle from difference between two slopes.
Angle calculation step for extended elbow image. (A): Locate middle points of arm and forearm from one-third of distance of both edges. (B): Calculate angle from difference between two slopes.

Figure 4

Angle calculation step for flexed elbow image. (A) Determine cutting point for classifying four reference lines. (B) Calculate angle from two slopes.
Angle calculation step for flexed elbow image. (A) Determine cutting point for classifying four reference lines. (B) Calculate angle from two slopes.

Figure 5

Bland–Altman plot of digital image analysis and goniometer. (A) Extension. (B) Flexion. (C) Elbow range of motion (ROM).
Bland–Altman plot of digital image analysis and goniometer. (A) Extension. (B) Flexion. (C) Elbow range of motion (ROM).

Figure 6

Bland–Altman plot of digital image analysis and inclinometer. (A) Extension. (B): Flexion. (C): Elbow range of motion (ROM).
Bland–Altman plot of digital image analysis and inclinometer. (A) Extension. (B): Flexion. (C): Elbow range of motion (ROM).

Figure 7

Conceptual illustration of systematic measurement bias between landmark-based method, goniometer and inclinometer, and contour-based DIPT. Green circles are lateral epicondyle location and green lines are imaginary lines of goniometer and inclinometer measurements. Pink lines are the result of using DIPT on fluoroscopic image. (A) Extension image. (B) Flexion image.
Conceptual illustration of systematic measurement bias between landmark-based method, goniometer and inclinometer, and contour-based DIPT. Green circles are lateral epicondyle location and green lines are imaginary lines of goniometer and inclinometer measurements. Pink lines are the result of using DIPT on fluoroscopic image. (A) Extension image. (B) Flexion image.

Comparison measurement of digital image processing method with digital goniometer and digital inclinometer

Angle measurementDigital image processingDigital goniometerDigital inclinometer
Mean ± SD (range)Mean ± SD (range)Mean ± SD (range)
Extension−2.01 ± 6.30 (−16.86 – 8.92)−6.67 ± 3.48 (−11.83 – 0.28)−6.74 ± 3.58 (−11.87 – 0.15)
Flexion146.80 ± 5.20 (137.05 – 157.16)141.28 ± 4.71 (129.98 – 148.80)142.79 ± 6.58 (129.97 – 155.97)
Total ROM148.81 ± 7.72 (135.89 – 164.70)147.95 ± 6.89 (132.12 – 160.53)149.55 ± 8.36 (131.63 – 167.27)

Bland–Altman analytic results and percentage of DIPT measurement error within 10° compared with goniometer and inclinometer

Angle measurementMean of difference (95% CI)Upper LOA (95% CI)Lower LOA (95% CI)Absolute maximal error†Within 10° of error (%)
Goniometer
Extension4.51 (3.14, 5.88)14.56 (12.2, 16.92)–5.54 (–7.9, –3.18)±10.0591.1
Flexion5.46 (4.12, 6.80)15.26 (12.96, 17.57)–4.35 (–6.66, –2.05)±9.8182.1
Total ROM0.94 (–0.95, 2.84)14.79 (11.54, 18.04)–12.90 (–16.16, –9.65)±13.8480.4
Inclinometer
Extension4.61 (3.25, 5.96)14.53 (12.2, 16.86)–5.32 (–7.65, –2.99)±9.9387.5
Flexion3.98 (2.45, 5.50)15.15 (12.52, 17.77)–7.19 (–9.81, –4.57)±11.1785.7
Total ROM–0.63 (–2.64, 1.38)14.10 (10.64, 17.56)–15.36 (–18.82, –11.9)±15.9983.9

The results of photography-based ROM of the elbow measurement from previous literatures compared with current study

StudyElbowsReference methodMean difference (°)95% LOA (°)Within 10° (%)
ExtensionFlexionROMExtensionFlexionROMExtensionFlexionROM
Blonna et al. [7]50DG017–318–21
Meislin et al. [1]64DG0.2–0.38.3–9.595
Keijsers et al. [8]80DG10
Russo et al. [4]20MCA411.716†93
Chanlalit and Kongmalai [5]60DG2.62.17.813.498.385
Current study (DG)56DG4.55.50.910.19.8113.891.182.180.4
Current study (DI)56DI4.64.00.69.911.21687.585.783.9
DOI: https://doi.org/10.1515/abm-2020-0006 | Journal eISSN: 1875-855X | Journal ISSN: 1905-7415
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Language: English
Page range: 37 - 44
Published on: Jul 13, 2020
Published by: Chulalongkorn University
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
Keywords:
artificial intelligence,
computer-assisted,
diagnostic techniques and procedures,
elbow joint,
image analysis,
range of motion
Related subjects:
Medicine,
Assistive professions, nursing,
Basic medical science,
Basic medical science, other,
Clinical medicine,
Clinical medicine, other

© 2020 Chris Charoenlap, Krerk Piromsopa, published by Chulalongkorn University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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