Title: A novel feature set for bone fixator classification from post-operative X-ray images

Authors: Mrityunjaya V. Latte; V. Kumar Swamy; Basavaraj S. Anami

Addresses: JSS Academy of Technical Education, Uttarahalli, Kengeri Road, Bengaluru, 56006, Karnataka, India ' Department of Electrical and Electronics Engineering, KLE Institute of Technology, Gokul Road, Hubli, 580030, Karnataka, India ' KLE Institute of Technology, Gokul Road, Hubli, 580030, Karnataka, India

Abstract: The paper presents a novel feature set for classification of X-ray images of bone fixators using artificial neural network. The images are obtained from radiologists. We have considered six types of bone fixators, namely, standard, ring, k-wire, screw, rod and plate. Use of both local and global features, wherein local features are defined in consultation with orthopaedicians. The feature set is reduced based on the classification accuracies of individual features. It is observed that the average accuracies for local, global and their combination are 76.83%, 66.16% and 98.3% respectively. The work finds its application in orthopaedics surgeries assisted by robots and second opinion for surgeons.

Keywords: image classification; artificial neural network; bone fixators.

DOI: 10.1504/IJMEI.2021.111862

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.1, pp.24 - 34

Received: 13 Aug 2018
Accepted: 02 Feb 2019
Published online: 18 Dec 2020 *

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