Plantar fascia ultrasound images characterization and classification using support vector machine

Boussouar, A ORCID: https://orcid.org/0000-0002-2674-1734, Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914 and Walton, LA ORCID: https://orcid.org/0000-0002-8651-7443 2019, Plantar fascia ultrasound images characterization and classification using support vector machine , in: The 5th International Conference on Advanced Intelligent Systems and Applications, 26-28 October 2019, Cairo, Egypt.

[img] PDF - Accepted Version
Restricted to Repository staff only until 2 October 2020.

Download (357kB) | Request a copy

Abstract

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: The 5th International Conference on Advanced Intelligent Systems and Applications (AISI 2019)
Publisher: Springer
Series Name: Advances in Intelligent Systems and Computing
ISBN: 9783030311285; 9783030311292
ISSN: 2194-5357
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 28 May 2019 13:16
Last Modified: 15 Nov 2019 11:00
URI: http://usir.salford.ac.uk/id/eprint/51434

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year