Call for Paper

CAE solicits original research papers for the January 2019 Edition. Last date of manuscript submission is December 31, 2018.

Read More

An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures

Faiyaz Mohammad Saif, Jabin Rubayat, Md.Hosne Al Walid. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Faiyaz Mohammad Saif, Jabin Rubayat, Md.Hosne Al Walid
10.5120/cae2018652794

Faiyaz Mohammad Saif, Jabin Rubayat and Md.Hosne Al Walid. An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures. Communications on Applied Electronics 7(22):14-20, November 2018. BibTeX

@article{10.5120/cae2018652794,
	author = {Faiyaz Mohammad Saif and Jabin Rubayat and Md.Hosne Al Walid},
	title = {An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures},
	journal = {Communications on Applied Electronics},
	issue_date = {November 2018},
	volume = {7},
	number = {22},
	month = {Nov},
	year = {2018},
	issn = {2394-4714},
	pages = {14-20},
	numpages = {7},
	url = {http://www.caeaccess.org/archives/volume7/number22/832-2018652794},
	doi = {10.5120/cae2018652794},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Distinguishing human hand fingers bone break is an extremely basic issue in restorative. The framework proposed another approach to recognize these sorts of crack by removing highlights. For doing the general procedure among MRI (Magnetic Resonance Imaging), CT (Computed Tomography) and X-beam pictures, the proposed framework utilized X-beam pictures. At first, the framework takes information from different orthopedic foundations. Subsequent to getting the information the picture preprocessing steps have been done: right off the bat pictures have been changed over into dark, at that point sifted lastly into parallel pictures. From twofold pictures the GLCM (Gray Level Co-event Matrix), minute highlights, entropy, real pivot length, minor hub length, erraticism, introduction, arched region, zone, filled zone, equiv breadth, robustness, degree, border, mean, standard deviation, relationship coefficient, middle, fluctuation, proportion, pixel, and Euclidian separation has been removed. The element esteems are prepared by the Artificial Neural Network (ANN) where the framework used to encourage forward back proliferation systems. At that point, the yield gives two qualities where it is in typical or crack. The figure of prepared Neural Network gives the execution, preparing state, relapse of the trial which is high. The tables decided the gatherings which are changed into size and shapes and furthermore gives that the pictures are in typical or crack with precision 92.24% which is superior to other. The proposed framework can effectively distinguish the pictures of crack and typical yet can't recognize its composes. Later on, the framework will attempt to test about it.

References

  1. Santoso.H. and Nakamura.K., “Situation Awareness Processing Based on Background and Foreground Image for Pedestrian,” in SCIS & ISIS SCIS & ISIS 2006, 2006; 949–54.
  2. Ayyoub.A., Hmeidi.M.I. and Rababah.H., “Detecting Hand Bone Fractures in X-Ray Images.,” JMPT, 2013; 4(3); 155–68.
  3. Khatik.I., “A Study of Various Bone Fracture Detection Techniques,” International Journal 0f Engineering And Computer Science, 2017; 6(5).
  4. Smith.R., “Segmentation and Fracture Detection in X-ray images for Traumatic Pelvic Injury,” 2010.
  5. Miah.M. B. A. and Yousuf.M. A., “Detection of lung cancer from CT image using image processing and neural network,” in Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference on, 2015; 1–6.
  6. Chai.H.Y., Wee.L. K., Swee.T.T., Salleh.S.H. and Ariff.A.K., “Gray-level co-occurrence matrix bone fracture detection,” American Journal of Applied Sciences, 2011; 8(1); 26.
  7. Chai.H.Y., Wee.L. K., Swee.T.T. and Hussain.S., “GLCM based adaptive crossed reconstructed (ACR) k-mean clustering hand bone segmentation,” Book GLCM based adaptive crossed reconstructed (ACR) k-mean clustering hand bone segmentation, 2011;192–7.
  8. Lehmann.T.M., Beier.D., Thies.C. and Seidl.T., “Segmentation of medical images combining local, regional, global, and hierarchical distances into a bottom-up region merging scheme,” in Medical Imaging 2005: Image Processing, 2005; 5747; 546–56.
  9. Syiam.M., El-Aziem.M.A. and El-Menshawy.M., “Adagen: Adaptive interface agent for x-ray fracture detection,” International Journal of Computing & Information Sciences, 2004; 2(3).
  10. Stolojescu.C. and Holban.S., “A comparison of X-ray image segmentation techniques,” Advances in Electrical and Computer Engineering, 2013; 13( 3).
  11. Peng.T.T., “Detection of Femur Fractures in X-ray images,” Master of Science Thesis, National University of Singapore, 2002.
  12. Lim.S. E., Xing.Y., Chen.Y, Leow.W.K., Howe T. S. and Png M. A., “Detection of femur and radius fractures in x-ray images,” in Proc. 2nd Int. Conf. on Advances in Medical Signal and Info. Proc, 2004; 65.
  13. Lum.V. L. F., Leow.W. K.., Chen Y., Howe T. S., and Png.M. A., “Combining classifiers for bone fracture detection in X-ray images,” in Image Processing, 2005. ICIP 2005. IEEE International Conference on, 2005;1; I–1149.
  14. Yap.D., W.H., Chen.Y., Leow.W. K., Howe.T. S., and Png.M. A., “Detecting femur fractures by texture analysis of trabeculae,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2004; 3; 730–33.
  15. Hao.S., Han.Y., Zhang.J., and Ji.Z., “Automatic isolation of carpal-bone in hand x-ray medical image,” in Informatics and Management Science I, Springer, 2013; 657–62.
  16. Lin.P., Zheng.C., Zhang.F., and Yang.Y., “X-ray carpal-bone image boundary feature analysis using region statistical feature based level set method for skeletal age assessment application.,” OpticaApplicata, 2005; 35(2).
  17. 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, 2008; 41(12); 3786–98.
  18. Zielinski.B., “A fully-automated algorithm dedicated to computing metacarpophalangeal and interphalangeal joint cavity widths,” SchedaeInformaticae, 2007;16; 47–67.
  19. Mahendran.S., Kand.B. S. S., “An enhanced tibia fracture detection tool using image processing and classification fusion techniques in X-ray images,” Global Journal of Computer Science and Technology, 2011; 11(14); 23–28.
  20. Version.M, “9.0. 0 (R2016a),” Math Works Inc., Natick, MA, USA, 2016.
  21. Alam.M.B., “Detection of Brain Cancer from MRI Images using Neural Network.
  22. Al-Amin.M., Miah.M.B.A. and Mia M.R., “.Detection of Cancerous and Non-cancerous Skin busing GLCM Matrix and Neural Network Classifier,’’ in International Journal of Computer Applications(IJCA),2015,132(8).
  23. Al-Ayyoub.M. and Al-Zghool.D., “Determining the type of long bone fractures in x-ray images,” WSEAS Transactions on Information Science and Applications, 2013; 10(8); 261–70.

Keywords

Hand fracture images, x-ray, GLCM, moment feature, neural network, classification.