한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제15권10호
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  • Pages.1205-1211
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  • 2012
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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Sparse 표현을 이용한 X선 흡수 영상 개선

X-ray Absorptiometry Image Enhancement using Sparse Representation

  • 김형일 (한국과학기술원 전기 및 전자공학과) ;
  • 엄원용 (한국과학기술원 전기 및 전자공학과) ;
  • 노용만 (한국과학기술원 전기 및 전자공학과)
  • 투고 : 2012.07.20
  • 심사 : 2012.08.21
  • 발행 : 2012.10.31
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초록

대사성 골 질환인 골다공증(Osteoporosis)의 조기 진단을 위해 X 선 영상에서 골 밀도를 측정하는 방법이 최근 연구되고 있다. 골 밀도는 X 선 영상에서 뼈가 분리되고, 분리된 영역에서의 픽셀에 의해 BMD가 측정되는데, 개선된 영상에서의 정밀한 뼈 추출이 주요한 요소이므로 X 선 영상의 개선은 골다공증의 조기 진단을 위해 필수적이다. 본 논문에서는 sparse 표현을 도입하여 다중(multiple) 잡음을 갖는 X 선 영상을 개선시키는 방법을 제안한다. 실험을 통해 제안한 방법의 결과가 기존의 방법인 웨이블릿 BayesShrink 잡음 제거 방법 및 일반적 sparse 표현 모델의 잡음 제거 방법의 결과에 비해 개선됨을 CNR(Contrast to Noise Ratio) 및 cut-view를 통해 확인하였다.

Recently, the evaluating method of the bone mineral density (BMD) in X-ray absorptiometry image has been studied for the early diagnosis of osteoporosis which is known as a metabolic disease. The BMD, in general, is evaluated by calculating pixel intensity in the bone segmented regions. Accurate bone region extraction is extremely crucial for the BMD evaluation. So, a X-Ray image enhancement is needed to get precise bone segmentation. In this paper, we propose an image enhancement method of X-ray image having multiple noise based sparse representation. To evaluate the performance of proposed method, we employ the contrast to noise ratio (CNR) metric and cut-view graphs visualizing image enhancement performance. Experimental results show that the proposed method outperforms the BayesShrink noise reduction methods and the previous noise reduction method in sparse representation with general noise model.

키워드

참고문헌

  1. Stephan Grampp, Radiology of Osteoporosis, Springer, Berlin Heidelberg, 2008.
  2. A. El Maghraoui and C. Roux, "DXA Scanning in Clinical Practice," Int'l Journal of Medicine , Vol. 101, No. 8, pp. 605-617, 2008.
  3. P.J. Ryan, "Overview of Role of BMD Measurements in Managing Osteoporosis," Seminars in Nuclear Medicine, Vol. 27, No. 3, pp. 197-209, 1997. https://doi.org/10.1016/S0001-2998(97)80024-4
  4. J.W. Kwon, S.I. Cho, Y.B. Ahn, and Y.M. Ro, "Bone Region Extraction by Dual Energy X-ray Absorption Image Decompositions," Journal of Korea Multimedia Society, Vol. 12, No. 9, pp. 1233-1241, 2009.
  5. J.W. Kwon, S.I. Cho, Y.B. Ahn, and Y.M. Ro, "Noise Reduction in DEXA Image Based on System Noise Modeling," Int'l Conf. Biomed. Phar. Eng., pp. 1-6, 2009.
  6. M.F. Hossain, M.R. Alsharif, and K. Yamashita, "Medical Image Enhancement Based on Nonlinear Technique and Logarithmic Transform Coefficient Histogram Matching," Int'l Conf. Complex Med. Eng., pp. 58-62, 2010.
  7. David L. Donoho, "De-Noising by Soft- Thresholding," IEEE Trans. Inf. Theory, Vol. 41, No. 3, pp. 613-627, 1995. https://doi.org/10.1109/18.382009
  8. L. Wang, J. Lu, Y. Li, T. Yahagi, and T. Okamoto, "Noise Removal for Medical X-ray Images in Wavelet Domain," IEEJ Trans. Elec. Info. Sys., Vol. 126, No. 2, pp. 237-244, 2006.
  9. X. Huang, A.C. Madoc, and A.D. Cheetham, "Image Multi-Noise Removal by Waveletbased Bayesian Estimator," Int'l Symp. Circuits and Syst., Vol. 3, pp. 2699-2702, 2005.
  10. M. Elad and M. Aharon, "Image Denoising via Sparse and Redundant Representations Over Learned Dictionaries," IEEE Trans. Image Processing, Vol. 15, No. 12, pp. 3736-3745, 2006. https://doi.org/10.1109/TIP.2006.881969
  11. J. Wright, A.Y. Yang, A. Genesh, S. Shankar Sastry, and Yi Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 31, No. 2, pp. 210-227, 2009. https://doi.org/10.1109/TPAMI.2008.79
  12. J. Yang, J. Wright, Thomas S. Huang, and Yi Ma, "Image Super-Resolution via Sparse Representation," IEEE Trans. Image Processing, Vol. 19, No. 11, pp. 2861-2873, 2010. https://doi.org/10.1109/TIP.2010.2050625
  13. Olg V. Michailovich and A. Tannenbaum, "Despeckling of Medical Ultrasound Images," IEEE Trans. Ultrason., Ferroelect.. Freq. Cont., Vol. 53, No. 1, pp. 64-78, 2006. https://doi.org/10.1109/TUFFC.2006.1588392
  14. Y.C. Pati, R. Rezaiifar, and P.S. Krishnaprasad, "Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition," The 27th Annu. Asilomar Conf. Signals, Systems, and Computers, Vol. 1, pp. 40-44, 1993.
  15. P. Bao and L. Zhang, "Noise Reduction for Magnetic Resonance Image via Adaptive Multiscale Products Thresholding," IEEE Trans. Med. Imag., Vol. 22, No. 9, pp. 1089-1099, 2003. https://doi.org/10.1109/TMI.2003.816958
  16. Y. Li, J. Lu, L. Wang, T. Yahagi, and T. Okamoto, "Removing Noise from Radiological Image using Multineural Network Filter," Int'l Conf. Indus. Technology, pp. 1365-1370, 2005.

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