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Diagnosis of X-Ray Using Gabor Wavelet Transform

Authors(2) :-Dr. S. S. Mungona, Miss. Nishigandha M. Sawarkar

The bone fracture is common problem in human beings due to accident or other causes like bone cancer etc. The fracture can occur in any bone of our body like wrist, heel, ankle, hip, rib, leg, chest etc. It is not possible to view fractures by naked eyes, so X-ray/CT images are used to detect it. But sometimes these images lack sufficient details needed to diagnose. Now a day’s image processing is playing an important role in bone fracture detection. Image processing is important in modern data storage and data transmission especially in progressive transmission of images, video coding (teleconferencing), digital libraries, image database, and remote sensing. This paper presents a study of image processing techniques for bone fracture detection. This paper will help user to study different methods for bone fracture detection using image processing and to design new techniques to improve accuracy of fracture detection. Wavelets have been widely used in signal and image processing. Wavelet transforms have been successfully applied to many topics including tomography reconstruction, image compression, noise reduction, image enhancement, texture analysis, segmentation, multiscale registration. This paper also presents technologies used to implement image processing based system for fracture detection with pros and cons.
Dr. S. S. Mungona, Miss. Nishigandha M. Sawarkar
X-rays, CT Images, Tomography.
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Aishwariya, R., Geetha, M.Kalaiselvi. and Archana, M. “Computer- Aided Fracture Detection Of X-Ray Image”,
  7. 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).
  8. 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.
  9. S.K.Mahendranα , S.Santhosh Baboo. An Enhanced Tibia Fracture Detection Tool Using Image Processing and Classification Fusion Techniques in X-Ray Images. In Global Journal of Computer Science and Technology Volume 11 Issue 14 Version 1.0 August 2011
  10. Anu T C, Mallikarjunaswamy M.S Rajesh Raman.(2012) Detection of Bone Fracture using Image Processing Methods. In International Journal of Computer Applications (0975 – 8887)
  11. Sachin R.Mahajan, P.H.Zope,S.R.Suralkar. Review of An Enhance Fracture Detection Algorithm Design Using XRays Image Processing. International Journal of Innovative Research in Science, Engineering and Technology, Vol. 1, Issue 2, December 2012.
  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. Mahmoud Al-Ayyoub, Ismail Hmeidi, Haya Rababah. Detecting Hand Bone Fractures in X-Ray Images. Journal of Multimedia Processing and Technologies Volume 4 Number 3 September 2013.
  14. Swathika.B1, Anandhanarayanan.K, Baskaran.B, and Govindaraj.R.Radius Bone Fracture Detection Using Morphological Gradient Based Image Segmentation Technique.
  15. Chokkalingam, SP. and Komathy, K. (2014). ?Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images. World Academy of Science, Engineering and Technology International Journal of Computer Information Systems and Control Engineering, Vol. 8, Issue .5, pp.834-843.
  16. Rathode, Hs. and Ali, Wahid. (2015), ?MRI Brain Image Quantification Using artificial neural networks – A Review Report, ISOI Journal of Engineering and Computer science, Vol. 1, Issue No. 1, pp. 48-55. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1616-1619 .
  17. Tanudeep Kaur , Anupam Garg.(2016) Bone Fraction Detection using Image Segmentation. International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 2- June 2016.
Publication Details
  Published in : Volume 4 | Issue 8 | May-June 2018
  Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 275-281
Manuscript Number : IJSRST184847
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Dr. S. S. Mungona, Miss. Nishigandha M. Sawarkar, "Diagnosis of X-Ray Using Gabor Wavelet Transform", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.275-281, May-June-2018
URL : http://ijsrst.com/IJSRST184847