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Detection of Bone Fracture using Image Processing Methods

Published on December 2015 by Anu T.c., Mallikarjunaswamy M.s., Rajesh Raman
National Conference on Power Systems and Industrial Automation
Foundation of Computer Science USA
NCPSIA2015 - Number 3
December 2015
Authors: Anu T.c., Mallikarjunaswamy M.s., Rajesh Raman
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Anu T.c., Mallikarjunaswamy M.s., Rajesh Raman . Detection of Bone Fracture using Image Processing Methods. National Conference on Power Systems and Industrial Automation. NCPSIA2015, 3 (December 2015), 6-9.

@article{
author = { Anu T.c., Mallikarjunaswamy M.s., Rajesh Raman },
title = { Detection of Bone Fracture using Image Processing Methods },
journal = { National Conference on Power Systems and Industrial Automation },
issue_date = { December 2015 },
volume = { NCPSIA2015 },
number = { 3 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 6-9 },
numpages = 4,
url = { /proceedings/ncpsia2015/number3/23340-7257/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Power Systems and Industrial Automation
%A Anu T.c.
%A Mallikarjunaswamy M.s.
%A Rajesh Raman
%T Detection of Bone Fracture using Image Processing Methods
%J National Conference on Power Systems and Industrial Automation
%@ 0975-8887
%V NCPSIA2015
%N 3
%P 6-9
%D 2015
%I International Journal of Computer Applications
Abstract

The bone fracture is a common problem in human beings occurs due to high pressure is applied on bone or simple accident and also due to osteoporosis and bone cancer. Therefore the accurate diagnosis of bone fracture is important aspects in medical field. In this work X-ray/CT images are used for bone fracture analysis. The aim of this project is to develop an image processing based efficient system for a quick and accurate classification of bone fractures based on the information gained from the x-ray / CT images. Images of the fractured bone are obtained from hospital and processing techniques like pre-processing, segmentation, edge detection and feature extraction methods are adopted. The processed images will be further classified into fractured and non-fractured bone and compare the accuracy of different methods. This project is fully employed MATLAB 7. 8. 0 as the programming tool for loading image, image processing and user interface development. Results obtained demonstrate the performance of the bone fracture detection system with some limitations and good accuracy of 85%.

References
  1. Vijaykumar, V. , Vanathi, P. , Kanagasabapathy, P. (2010). Fast and efficient algorithm to remove gaussian noise in digital images. IAENG International Journal of Computer Science, 37(1).
  2. Al-Khaffaf, H. , Talib, A. Z. , Salam, R. A. (2008). Removing salt-and-pepper noise from binary images of engineering drawings. In: Pattern Recognition. ICPR. 19th International Conference on, p. 1–4. IEEE.
  3. Zain, M. L. M. , Elamvazuthi, I. , Begam, M. (2009). Enhancement of bone fracture image using filtering techniques. The International Journal of Video and Image Processing and Network Security, 9 (10).
  4. Chan, K. -P. , Fu, A. W. -C. (1999). Efficient time series matching by wavelets. Data Engineering. In: Proceedings, 15th International Conference on, p. 126–133. IEEE.
  5. Tian, T. (2002). Detection of femur fractures in x-ray images. Master's thesis, National University of Singapore, Singapore.
  6. Lim, S. E. , Xing, Y. , Chen, Y. , Leow, W. K. , Howe, T. S. , Png, M. A. (2004). Detection of femur and radius fractures in x-ray images. In: Proc. 2nd Int. Conf. on Advances in Medical Signal and Info. Proc.
  7. Yap, D. W. -H. , Chen, Y. , Leow, W. K. , Howe, T. S. , Png, M. A. (2004). Detecting femur fractures by texture analysis of trabeculae. In Pattern Recognition. ICPR. In: Proceedings of the 17th International Conference on, 3, p. 730–733. IEEE.
  8. Lum, V. L. F. , Leow, W. K. , Chen, Y. , Howe, T. S. , Png, M. A. (2005). Combining classifiers for bone fracture detection in x-ray images. In: Image Processing. ICIP. IEEE International Conference on, 1, p. I–1149. IEEE.
  9. He, J. C. , Leow, W. K. , Howe, T. S. (2007). Hierarchical classifiers for detection of fractures in x-ray images. In Computer Analysis of Images and Patterns, p. 962–969. Springer.
  10. Mahendran, S. , Baboo, S. S. (2011). An enhanced tibia fracture detection tool using image processing and classification fusion techniques in X-ray images. Global Journal of Computer Science and Technology (GJCST), 11 (14) 23–28.
  11. Chai, H. Y. , Wee, L. K. , Swee, T. T. , Hussain, S. (2011). Glcm based adaptive crossed reconstructed (acr) k-mean clustering hand bone segmentation. p. 192–197.
  12. Hao, S. , Han, Y. , Zhang, J. , Ji, Z. (2013). Automatic isolation of carpal-bone in hand x-ray medical image. In Informatics and Management Science I, p. 657–662. Springer.
  13. Bielecki, A. , Korkosz, M. , Zielinski, B. Hand radiographs preprocessing, image representation in the finger regions and joint space width measurements for image interpretation. Pattern Recognition, 41(12) 3786–3798, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Bone Fracture Noise Removal Segmentation Classification.