Login | Register

Computer Vision-Based Guidance Tool for Correct Radiographic Hand Positioning


Computer Vision-Based Guidance Tool for Correct Radiographic Hand Positioning

Portafaix, Aloys (2023) Computer Vision-Based Guidance Tool for Correct Radiographic Hand Positioning. Masters thesis, Concordia University.

[thumbnail of Portafaix_MCompSc_S2023.pdf]
Text (application/pdf)
Portafaix_MCompSc_S2023.pdf - Accepted Version
Restricted to Repository staff only until 1 August 2025.
Available under License Spectrum Terms of Access.


Hand X-Rays are used for tasks such as detecting fractures and investigating joint pain. The choice of the X-Ray view plays a crucial role in a medical expert's ability to make an accurate diagnosis. In particular, this choice is essential for the hand, where small and overlapping bones of the carpals can make it challenging to view structures even with correct positioning. In this study, we develop a prototype that uses deep learning models, iterative methods and a depth sensor to estimate hand and X-Ray machine parameters. We then use these parameters to generate feedback that ensures proper positioning according to radiographic positioning standards.

The method of this study consists of five steps: detector plane parameter estimation, 2D hand keypoint prediction, landmark depth estimation, positioning parameter extraction, and protocol constraint verification. Detector plane parameter estimation is achieved by fitting a plane to randomly queried depth points using RANSAC. Google's MediaPipe HandPose model is used for 2D hand keypoint estimation, and depth coordinates are determined using the OAK-D Pro's depth sensor. Finally, hand positioning parameters are extracted and evaluated with respect to the selected viewing protocol. We focus on three commonly used hand positioning protocols: posterior-anterior, oblique, and lateral view. The prototype also has a user interface and a feedback system designed for practical use in the X-Ray room.

Two evaluations are undertaken to validate our prototype. First, with the help of a radiology technician, we rate the quality of the suggested positioning by the device. Second, using a bespoke left-hand X-ray phantom and an X-Ray machine, we generate images with and without the prototype guidance for a double-blind study rated by a radiologist.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Portafaix, Aloys
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:14 August 2023
Thesis Supervisor(s):Fevens, Thomas
ID Code:992737
Deposited By: Aloys Portafaix
Deposited On:14 Nov 2023 20:37
Last Modified:14 Nov 2023 20:37
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top