KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Image Similarity Measurement based on MR Physical Information

  • Received : 2016.09.27
  • Accepted : 2017.05.25
  • Published : 2017.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, introduction of the hospital information system has remarkably improved the efficiency of health care services within hospitals. Due to improvement of the hospital information system, the issue of integration of medical information has emerged, and attempts to achieve it have been made. However, as a preceding step for integration of medical information, the problem of searching the same patient should be solved first, and studies on patient identification algorithm are required. As a typical case, similarity can be calculated through MPI (Master Patient Index) module, by comparing various fields such as patient's basic information and treatment information, etc. but it has many problems including the language system not suitable to Korean, estimation of an optimal weight by field, etc. This paper proposes a method searching the same patient using MRI information besides patient's field information as a supplementary method to increase the accuracy of matching algorithm such as MPI, etc. Unlike existing methods only using image information, upon identifying a patient, a highest weight was given to physical information of medical image and set as an unchangeable unique value, and as a result a high accuracy was detected. We aim to use the similarity measurement result as secondary measures in identifying a patient in the future.

Keywords

References

  1. Hyoungsuk Moon, Kyhyun Um, "Design of Medical Image Retrieval System," KORMARC, pp.315-318, 2002.
  2. S. Hemachande, A. Verma, S. Arora, and Prasanta K. Panigrahi, "Locally Adaptive Block Thresholding Method with Continuity Constraint," Pattern Recognition Letters, 28, pp. 119-124, 2007. https://doi.org/10.1016/j.patrec.2006.06.005
  3. C. C. Kang and W. J. Wang, "A Novel Edge Detection Method Based on Maximization of the Objective Function," Pattern Recognition, Vol. 40, No. 2, pp. 609-618, 2007. https://doi.org/10.1016/j.patcog.2006.03.016
  4. Rafael C. Gonzalez and Paul Wintz, "Digital Image Processing, Software Engineering A Practltiners' Approach," 3rd Ed., Addison-Wesley.Roger S. Pressman, 3rd Ed. McGraw Hill, 1993.
  5. Haacke EM, Ayaz M, Khan A, et al, "Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs. abnormal iron content in the brain," J Magn Reson Imaging 26:256-64, 2007. https://doi.org/10.1002/jmri.22987
  6. Ogawa S, Lee TM, Kay AR, Tank DW, "Brain magnetic resonance imaging with contrast dependent on blood oxygenation," in Proc of Natl Acad SciUSA, 87:9868-9872, 1990. https://doi.org/10.1073/pnas.87.24.9868
  7. Ogawa S, Lee TM, "Magnetic resonance imaging of blood vessels at high fields: in vivo and in vitro measurements and image simulation," Magn Reson Med, 16:9-18, 1990. https://doi.org/10.1002/mrm.1910160103
  8. Ogawa S, Lee TM, Nayak AS, Glynn P., "Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields," Magn Reson Med, 14:68-78, 1990. https://doi.org/10.1002/mrm.1910140108
  9. Ogg RJ, Langston JW, Haacke EM, Steen RG, Taylor JS, "The correlation between phase shifts in gradient-echo MR images and regional brain iron concentration," Magn Reson Imaging, 17:1141-1148, 1999. https://doi.org/10.1016/S0730-725X(99)00017-X
  10. Haacke, EM, Patrick JL, Lenz GW, Parrish T., "The separation of water and lipid components in the presence of field inhomogeneities," Rev Magn Reson Med, 1:123-154, 1986.
  11. P.B. Kingsley, "Concepts in Magn," Reson., 11, pp.29-49, 1999.
  12. E.M. Haacke, R.W. Brown, M.R. Thompson, R. Venkatesan, "Magnetic Resonance Imaging:Physical Principles and Sequence Design," John Wiley & Sons Inc., USA., pp.129-133, 1999.
  13. Reichenbach JR, Essig M, Haacke EM, Lee BC, Przetak C, Kaiser WA, Schad LR, "High resolution venography of the brain using magnetic resonance imaging," MAGMA, 6:62-69, 1998. https://doi.org/10.1007/BF02662513
  14. Essig M, Reichenbach JR, Schad LR, Schonberg SO, Debus J, Kaiser WA, "High-resolution MR venography of cerebral arteriovenous malformations," Magn Reson Imaging, 17:1417-1425, 1999. https://doi.org/10.1016/S0730-725X(99)00084-3
  15. Lee BCP, Vo KD, Kido DK, Mukherjee P, Reichenbach J, Lin W, Yoon MS, Haacke EM, "MR high-resolution blood oxygenation level-dependent venography of occult (low-flow) vascular lesions," AJNR Am J Neuroradiol, 20:1239-1242, 1999.
  16. Tan IL, van Schijndel RA, Pouwels PJW, van Walderveen MAA,Reichenbach JR, Manoliu RA, Barkhof F, "MR venography of multiple sclerosis," AJNR Am J Neuroradiol, 21:1039-1042, 2000.
  17. Fernandez-Seara MA, Techawiboonwong A, Detre JA, et al., "MR susceptometry for measuring global brain oxygen extraction," Magn Reson Med, 55:967-73, 2006. https://doi.org/10.1002/mrm.20892
  18. Baudendistel KT, Reichenbach JR, Metzner R, Schroder J, Schad LR., "Comparison of functional venography and EPI-BOLD-fMRI at 1.5T," Magn Reson Imaging, 16:989-991, 1998. https://doi.org/10.1016/S0730-725X(98)00085-X
  19. Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM, "Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent," Radiology, 204:272-277, 1997. https://doi.org/10.1148/radiology.204.1.9205259
  20. S. Hemachande, A. Verma, S. Arora, and Prasanta K. Panigrahi, "Locally Adaptive Block Thresholding Method with Continuity Constraint," Pattern Recognition Letters, 28, pp. 119-124, 2007. https://doi.org/10.1016/j.patrec.2006.06.005
  21. J-L. Rose, C. Revol-Muller, M. Almajdub, E. Chereul, and C. Odet, "Shape prior integrated in an automated 3d region growing method," in Proc. of Image Processing, 2007, ICIP 2007. IEEE International Conference on, vol. 1, pp. 53- 56, 2007.
  22. M. Vetterli, J. Kovacevic, "Wavelets and Subband Coding," Signal Processing