• Jan 21, 2020 News! Vol.11, No.1- Vol.11, No.3 has been indexed by EI(Inspec)!   [Click]
  • Apr 27, 2020 News!Vol.12, No. 2 has been published with online version.   [Click]
  • Feb 08, 2017 News!Welcome Assoc. Prof. Lei Chen from China to join the Editorial board of IJET.   [Click]
General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusEI (INSPEC, IET).
    • E-mail: ijet@vip.163.com
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2012 Vol.4(1): 7-10 ISSN: 1793-8236
DOI: 10.7763/IJET.2012.V4.310

Ensemble Systems for Automatic Fracture Detection

S. K. Mahendran and S. Santhosh Baboo

Abstract—Fracture detection based on image classification is an area of research which has proved to be challenging for the past several decades. This field has gained more attention due to the new challenges posed by voluminous image databases. In this research work, fusion-based classifiers are constructed, which extracts features from the images, use these features to train and test the classifiers for the purpose of detecting fractures in X-Ray images. The various features extracted are Contrast, Homogeneity, Energy, Entropy, Mean, Variance, Standard Deviation, Correlation, Gabor orientation (GO), Markov Random Field (MRF), and intensity gradient direction (IGD). Three classifiers, BPNN, SVM and NB classifiers are used. Using these features and classifiers, three single classifiers and four multiple classifiers were developed. All the classifiers were tested vigorously with the test dataset for evaluating the winner combination of classifiers and features that correctly identifies fractures in a bone image. The performance metrics used are sensitivity, specificity, positive predictive value, negative predictive value, accuracy and execution time. The experimental results showed that usage of fusion classifiers enhances the detection capacity and the combination SVM and BPNN produces the best result.

Index Terms—X-ray image, gabor orientation (GO), markov random field (MRF), intensity gradient direction (IGD), BPNN, SVM and NB.

S. K. Mahendran is with the Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India (e-mail: sk.mahendran@yahoo.co.in).
S. Santhosh Baboo is with the Postgraduate and Research department of Computer Science at Dwaraka Doss Goverdhan Doss Vaishnav College, Chennai, Tamil Nadu. India (e-mail: santhos2001@sify.com).


Cite: S. K. Mahendran and S. Santhosh Baboo, "Ensemble Systems for Automatic Fracture Detection," International Journal of Engineering and Technology vol. 4, no. 1, pp. 7-10, 2012.

Copyright © 2008-2020. International Journal of Engineering and Technology. All rights reserved. 
E-mail: ijet@vip.163.com