A novel feature set for bone fixator classification from post-operative X-ray images. (17th November 2020)

• Record Type: Journal Article
• Authors: Latte, Mrityunjaya V.; Swamy, V. Kumar; Anami, Basavaraj S.
• Is Part Of: International journal of medical engineering and informatics. Volume 13:Number 1(2021)
• Page Start: 24
• Page End: 34
• Publication Date: 2020-11-17
• Subjects: image classification -- artificial neural network -- bone fixators; 610.2805
• Languages: English
• ISSNs: 1755-0653

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.