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A Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners
Javad Razjouyan1, Omid Khayat, Mehdi Siahi
1Department of Electrical and Electronics Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran.
Journal of Medical Signals and Sensors
|October 2, 2013
Summary
This study introduces a hybrid algorithm using gray level spatial correlation (GLSC) histogram and Shanbag entropy for analyzing digital foot scans. The method accurately extracts anthropometric data from plantar images, optimizing parameters with an evolutionary algorithm.
Area of Science:
- Biomedical Engineering
- Computer Vision
- Anthropometry
Background:
- Digital foot scanners provide pressure distribution and anthropometric data.
- Accurate analysis of plantar images is crucial for biomechanical and clinical applications.
Purpose of the Study:
- To present a hybrid algorithm for analyzing scanned foot images.
- To optimize parameters using an evolutionary algorithm for enhanced accuracy.
- To perform anthropometric measurements from thresholded plantar images.
Main Methods:
- A hybrid algorithm combining GLSC histogram and Shanbag entropy.
- Utilizing an evolutionary algorithm for parameter optimization.
- Applying a scale factor for pixel-to-metric conversion.
Main Results:
- The algorithm successfully generates binary images for anthropometric measurements.
- Investigated computation time and the impact of GLSC parameters.
- Validated the method on scanned feet from randomly selected subjects.
Conclusions:
- The proposed hybrid algorithm offers a robust method for digital foot image analysis.
- Accurate anthropometric data can be extracted from plantar scans.
- The evolutionary algorithm aids in optimizing the analysis process.
