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Fracture detection in X-ray images of long bone

B. Gajjar1 , S. Patel2 , A.Vaghela 3

  1. Indus Institute of Technology and Engineering, Indus University, Ahmedabad, India.
  2. Indus Institute of Technology and Engineering, Indus University, Ahmedabad, India.
  3. Indus Institute of Technology and Engineering, Indus University, Ahmedabad, India.

Correspondence should be addressed to: bhavin50@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-6 , Page no. 129-133, Jun-2017

Online published on Jun 30, 2017

Copyright © B. Gajjar, S. Patel, A.Vaghela . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: B. Gajjar, S. Patel, A.Vaghela, “Fracture detection in X-ray images of long bone,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.129-133, 2017.

MLA Style Citation: B. Gajjar, S. Patel, A.Vaghela "Fracture detection in X-ray images of long bone." International Journal of Computer Sciences and Engineering 5.6 (2017): 129-133.

APA Style Citation: B. Gajjar, S. Patel, A.Vaghela, (2017). Fracture detection in X-ray images of long bone. International Journal of Computer Sciences and Engineering, 5(6), 129-133.

BibTex Style Citation:
@article{Gajjar_2017,
author = {B. Gajjar, S. Patel, A.Vaghela},
title = {Fracture detection in X-ray images of long bone},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2017},
volume = {5},
Issue = {6},
month = {6},
year = {2017},
issn = {2347-2693},
pages = {129-133},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1313},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1313
TI - Fracture detection in X-ray images of long bone
T2 - International Journal of Computer Sciences and Engineering
AU - B. Gajjar, S. Patel, A.Vaghela
PY - 2017
DA - 2017/06/30
PB - IJCSE, Indore, INDIA
SP - 129-133
IS - 6
VL - 5
SN - 2347-2693
ER -

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Abstract

Image processing used in wide variety of applications such as Image restorations, satellite, and medical etc. With enrichments of the image processing libraries especially of openCV and Matlab, many applications are being developed day by day in computer vision or image processing domain. We have designed the bone fracture detection method using “Image Processing” toolbox in Matlab. Aim of the project is to locate exact fracture area in inputted X-ray image. We will check bone integrity to detect any crack or disjoint of two cartilages. The professed algorithm is divided in few step namely pre-processing, Segmentation and ROI search and detection. Features like area, length and pixel locations of the segments are used to identify fracture in X-ray image. Algorithm has been simulated on various X-ray images which show good results to locate fracture in image. Also, in this approach we found that canny edge detection works far better than any other edge detection for segmenting the fractured part.

Key-Words / Index Term

Atutomatic, Bone, Canny, Fracture, Image Processing, Preprocessing,Segmentation, X-Ray

References

[1] Jacob, Nathanael E., and M. V. Wyawahare. "Survey of Bone Fracture Detection Techniques." International Journal of Computer Applications 71.17, 2013.
[2] Jia,Y.,and Y.Jiang. "Active contour model with shape constraints for bone fracture detection." International Conference on Computer Graphics, Imaging and Visualisation. IEEE, 2006.
[3] Liang, Jian, et al. "Fracture identification of X-ray image." International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), IEEE, 2010.
[4] Donnelley, Martin William. “Computer aided long-bone segmentation and fracture detection.” Diss. Flinders University, Faculty of Science and Engineering, 2008.
[5] Syiam, Mostafa, Mostafa Abd El-Aziem, and Mohamed El-Menshawy. "AdAgen: Adaptive Interface Agent for X-Ray Fracture Detection." International Journal of Computing & Information Sciences 2.3, 2004.
[6] Wei, Zheng, and Zhang Liming. "Study on recognition of the fracture injure site based on X-ray images." 3rd International Congress on Image and Signal Processing (CISP), Vol. 4. IEEE, 2010.
[7] Smith, Rebecca, et al. "Detection of fracture and quantitative assessment of displacement measures in pelvic X-RAY images." International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE, 2010
[8] Linda, C. Harriet, and G. Wiselin Jiji. "Crack detection in X-ray images using fuzzy index measure." International Journal of Applied Soft Computing 11.4, pp.3571-3579, 2011
[9] M P Deshmukh, P. Deshmukh, “Development of Automatic Fracture Detection System using Image Processing and Classification Methods for Femur Bone X-Ray Images”, International Journal of Computer Sciences and Engineering (IJCSE), Vol. 4, Issue.1, pp.56-60, 2016.
[10] Shrivakshan, G. T., and C. Chandrasekar. "A comparison of various edge detection techniques used in image processing." IJCSI International Journal of Computer Science Issues 9.5, pp.272-276, 2012.