Transactions on Electrical and Electronic Materials

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
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CAD for Detection of Brain Tumor Using the Symmetry Contribution From MR Image Applying Unsharp Mask Filter

  • Kim, Dong-Hyun (Department of Radiological Science, Catholic University of Pusan) ;
  • Ye, Soo-Young (Department of Radiological Science, Catholic University of Pusan)
  • Received : 2014.06.30
  • Accepted : 2014.07.15
  • Published : 2014.08.25
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Abstract

Automatic detection of disease helps medical institutions that are introducing digital images to read images rapidly and accurately, and is thus applicable to lesion diagnosis and treatment. The aim of this study was to apply a symmetry contribution algorithm to unsharp mask filter-applied MR images and propose an analysis technique to automatically recognize brain tumor and edema. We extracted the skull region and drawed outline of the skull in database of images obtained at P University Hospital and detected an axis of symmetry with cerebral characteristics. A symmetry contribution algorithm was then applied to the images around the axis of symmetry to observe intensity changes in pixels and detect disease areas. When we did not use the unsharp mask filter, a brain tumor was detected in 60 of a total of 95 MR images. The disease detection rate for the brain was 63.16%. However, when we used the unsharp mask filter, the tumor was detected in 87 of a total of 95 MR images, with a disease detection rate of 91.58%. When the unsharp mask filter was used in the pre-process stage, the disease detection rate for the brain was higher than when it was not used. We confirmed that unsharp mask filter can be used to rapidly and accurately to read many MR images stored in a database.

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References

  1. K. Doi, Comput. Med. Imaging Graph., 31, 198 (2007) [DOI: http://dx.doi.org/10.1016/j.compmedimag.2007.02.002].
  2. H. Arimura, T. Magome, Y. Yamashita, and D. Yamamoto, Algorithms, 2, 925 (2009) [DOI: http://dx.doi.org/10.3390/a2030925].
  3. H. P. Chan, J. Wei, Y. Zhang, and M. A. Helvie, Med. Phys., 35, 4087 (2008) [DOI: http://dx.doi.org/10.1118/1.2968098].
  4. K. Selvanayaki, M. Karnan, International Journal of Engineering Science and Technology, 2, 5890 (2010).
  5. X. Llado, O. Ganiler, A. Oliver, R. Marti, J. Freixenet, L. Valls, J. C. Vilanova, L. Ramio-Torrenta, and A. Rovira, Neuroradiology, 54, 787 (2012) [DOI: http://dx.doi.org/10.1007/s00234-011-0992-6].
  6. L. Lemieux, U. C. Wieshmann, N. F. Moran, D. R. Fish, and S. D. Shorvon, Med. Image Anal., 2, 224 (1998) [DOI: http://dx.doi.org/10.1016/ S1361-8415(01)80039-6].
  7. M. Bosc, F. Heitz, J. P. Armspach, I. Namer, D. Gounot, and L. Rumbach, NeuroImage, 20, 643 (2003) [DOI: http://dx.doi.org/10.1016/S1053-8119(03)00406-3].
  8. G. Szekely, D. Welti, G. Gerig, E. W. Radu, and L. Kappos, Inf. Proc. Med. Imaging, 2082, 438 (2001) [DOI: http://dx.doi.org/10.1007/3-540-45729-1_46].
  9. D. Rey, G. Subsol, H. Delingette, and N. Ayache, Med. Image Anal., 6, 163 (2002) [DOI: http://dx.doi.org/10.1016/S1361-8415(02)00056-7].
  10. H.S.S. Ahmed and M. J. Nordin Journal of Computer Science, 7, 1831 (2011) [DOI: http://dx.doi.org/10.3844/jcssp.2011.1831.1838 ].
  11. D. Reisfeld, H. Wolfson, and Y. Yeshurun, International Journal of Computer Vision, 14, 119 (1995) [DOI: http://dx.doi.org/10.1007/BF01418978].
  12. D.M.N. Mubarak, M. M. Sathik, S. Z. Beevi, and K. Revathy, International Journal of Computer Science & Information Technology, 4, 61 (2012) [DOI: http://dx.doi.org/10.5121/ijcsit.2012.4306. 61].
  13. E. A. Zanaty, American Journal of Remote Sensing, 1, 53 (2013) [DOI: http://dx.doi.org/10.11648/j.ajrs.20130102.16].
  14. N. Ommundsen, K. Engedal, and A. R. Oksengard, Dement Geriatr Cogn. Disord., 31, 195 (2011) [DOI: http://dx.doi.org/10.1159/000324878].
  15. L. S. Prichep, Clin. EG. Neurosci., 36, 82 (2005) [DOI: http://dx.doi.org/10.1177/155005940503600207].
  16. B. C. Jung, S. I. Choi, A. X. Du, J. L. Cuzzocreo, Z. Z. Geng, H. S. Ying, S. L. Perlman, A. W. Toga, J. L. Prince, and S. H. Ying, Cerebellum., 11, 887 (2012) [DOI: http://dx.doi.org/10.1007/s12311-011-0334-6].
  17. M. J. Bruwer, J. F. MacGregor, and M. D. Noseworthy, Journal of Chemometrics, 22, 708 (2008) [DOI: http://dx.doi.org/10.1002/cem.1143]
  18. J. Wu, S. Poehlman, M. D. Noseworthy, and M. V. Kamath, Journal of Biomedical Science and Engineering, 2, 1 (2009) [DOI:http://dx.doi.org/10.4236/jbise.2009. 21001].
  19. P. Svolos, E. Tsolaki, E. Kapsalaki, K. Theodorou, K. Fountas, I. Fezoulidis, and I. Tsougos, Magn Reson Imaging., 31, 1567 (2013) [DOI: http://dx.doi.org/10.1016/j.mri.2013.06.010].
  20. A. Melbourne, D. Atkinson, M. J. White, D. Collins, M. Leach, and D. Hawkes, Phys. Med. Biol., 52, 5147 (2007) [DOI: http://dx.doi.org/10.1088/0031-9155/52/17/003].