Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Effective Gray-white Matter Segmentation Method based on Physical Contrast Enhancement in an MR Brain Images

MR 뇌 영상에서 물리기반 영상 개선 작업을 통한 효율적인 회백질 경계 검출 방법

  • 은성종 (가천대학교 일반대학원 전자계산학과) ;
  • 황보택근 (가천대학교 IT대학 컴퓨터미디어융합과)
  • Received : 2013.04.04
  • Accepted : 2013.06.30
  • Published : 2013.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In medical image processing field, object recognition is usually carried out by computerized processing of various input information such as brightness, shape, and pattern. If the information mentioned does not make sense, however, many limitations could occur with object recognition during computer processing. Therefore, this paper suggests effective object recognition method based on the magnetic resonance (MR) theory to resolve the basic limitations in computer processing. We propose the efficient method of robust gray-white matter segmentation by texture analysis through the Susceptibility Weighted Imaging (SWI) for contrast enhancement. As a result, an average area difference of 5.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

의료 영상처리 분야에서의 일반적인 객체 인식 방법은 픽셀들의 밝기 정보, 형태 정보, 패턴 정보 등 다양한 컴퓨팅 처리 방법으로 수행되어 진다. 그러나 컴퓨팅 방법에 사용되는 다양한 정보들이 의미가 없을 경우 객체인식에 많은 제약이 따르게 된다. 따라서 본 논문은 이러한 컴퓨팅 처리의 근본적인 제약사항을 해결하고자, MR 의료 영상에서의 물리적인 이론에 기반한 영상처리 방법을 전처리에 활용하고자 한다. 제안된 방법은 대비 개선 작업을 주된 목적으로 하는 SWI(Susceptibility Weighted Imaging) 처리를 통해 의미 있는 전처리 작업을 수행하고, 이에 대한 결과를 텍스처 분석을 통해 MR 뇌 영상의 회백질을 효과적으로 검출하는 과정으로 구성된다. 실험결과 제안 방법은 평균 영역차이가 5.2%로 기존의 대표적인 영역분할 방법에 비해 보다 효율적임을 증명하였다.

Keywords

References

  1. Lih-Shyang Chen and R. Marc, Sontag, "Representation, Display, Manipulation of 3D Digital Scene and Their Medical Application," Computer Graphisc and Image Processing, Vol.48, pp.190-216, 1992.
  2. S. Hemachande, A. Verma, S. Arora, and Prasanta K. Panigrahi, Locally Adaptive Block Thresholding Method with Continuity Cons traint. Pattern Recognition Letters, Vol.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. R. C. Gonzalez and P. Wintz, Digital Image Processing, 3rd Ed., Addison-Wesley, 1993.
  5. In-Kue Park, Hyeon-Cheol Pak, A Study on Edge Detection of Fuzzy Entropy using Variable Length, Journal of digital contents society, Vol.9, No.2, pp.357-362,2008.
  6. R. I. Shrager, G. H. Weiss, and R. G. S. Spence, NMR Biomed., Vol.11, pp.297-305, 1998. https://doi.org/10.1002/(SICI)1099-1492(199810)11:6<297::AID-NBM531>3.0.CO;2-A
  7. R. V. Damadian, Science, Vol.171, pp.1151-1153, 1971. https://doi.org/10.1126/science.171.3976.1151
  8. R. A. de Graaf, P. B. Brown, S. McIntyre, T. W. Nixon, K. L. Behar, and D. L. Rothman, Magn, Reson.Med., Vol.56, pp.386-394, 2006. https://doi.org/10.1002/mrm.20946
  9. 김현호, 하본철, 김동원, 박태련, 강준식, "Brain MRI에서 기저핵 부위의 미세출혈 발견율 향상을 위한 SWI의 유용성",대한자기공명기술학회, 제17권,1호, pp.92-98, 2007.
  10. Goldstein RM, Zebker HA, Werner CL. Satellite radar interferometry: two-dimensional phase unwrapping. Radio Sci, Vol.23, pp.713-720, 1988. https://doi.org/10.1029/RS023i004p00713
  11. Essig M, Reichenbach JR, Schad LR, Schonberg SO, Debus J, Kaiser WA. High-resolution MR venography of cerebral arteriovenous malformations.Magn Reson Imaging 1999;17:1417-1425. https://doi.org/10.1016/S0730-725X(99)00084-3
  12. 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 JNeuroradiol 1999;20:1239-1242.
  13. Dixon WT. Simple proton spectroscopic imaging. Radiology 1984;153:189-194. https://doi.org/10.1148/radiology.153.1.6089263
  14. Schneider E, Glover G. Rapid in vivo proton shimming. Magn Reson Med 1991;18:335-347. https://doi.org/10.1002/mrm.1910180208
  15. Chavez S, Xiang QS, An L. Understanding phase maps in MRI: a new cutline phase unwrapping method. IEEE Trans Med Imaging 2002;21:966-977. https://doi.org/10.1109/TMI.2002.803106
  16. Ghiglia DC, Pritt MD. Two-dimensional phase unwrapping: theory, algorithms, and software. New York: John Wiley & Sons; 1998.
  17. Szumowski J, Coshow WR, Li F, Quinn SF. Phase unwrapping in the three-point Dixon method for fat suppression MR imaging. Radiology 1994;192:555-561. https://doi.org/10.1148/radiology.192.2.8029431
  18. Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 1991;18:371-383. https://doi.org/10.1002/mrm.1910180211
  19. S.E. Umbaugh, "Computer Imaging: Digital Image Analysis and Processing", CRC Press, Florida, 2005
  20. B. Chande and D. Dutta Majumder, "A note on the graylevel co-occurrence matrix in threshold selection," Signal Processing, Vol.15, No.2, 1988(9).
  21. N. Otsu, "A Thresholding Selection Method from Gray-scale Histogram," In IEEE Transactions on System, Man, and Cybernetics, Vol.9, No.1, pp.62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  22. J. L Muerle and D. C.Allen, Experimental Evaluation of a Technique for Automatic Segmentation of Objects in Complex Scenes. IPPR, Thopmson, 1968.
  23. M. Kass and A. Witkin, Demetri Terzopoulos Active Contour Models. International Journal of Computer Vision, Vol.1, pp.321-331, 1988. https://doi.org/10.1007/BF00133570

Cited by

  1. Phase Image of Susceptibility Weighted Image Using High Pass Filter Improved Uniformity vol.15, pp.11, 2014, https://doi.org/10.5762/KAIS.2014.15.11.6702