The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Automatic Left Ventricle Segmentation Algorithm using K-mean Clustering and Graph Searching on Cardiac MRI

K-평균 클러스터링과 그래프 탐색을 통한 심장 자기공명영상의 좌심실 자동분할 알고리즘

  • 조현우 (국립금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 이해연 (국립금오공과대학교 컴퓨터공학부)
  • Received : 2010.10.06
  • Accepted : 2011.12.01
  • Published : 2011.04.30
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Abstract

To prevent cardiac diseases, quantifying cardiac function is important in routine clinical practice by analyzing blood volume and ejection fraction. These works have been manually performed and hence it requires computational costs and varies depending on the operator. In this paper, an automatic left ventricle segmentation algorithm is presented to segment left ventricle on cardiac magnetic resonance images. After coil sensitivity of MRI images is compensated, a K-mean clustering scheme is applied to segment blood area. A graph searching scheme is employed to correct the segmentation error from coil distortions and noises. Using cardiac MRI images from 38 subjects, the presented algorithm is performed to calculate blood volume and ejection fraction and compared with those of manual contouring by experts and GE MASS software. Based on the results, the presented algorithm achieves the average accuracy of 6.2mL${\pm}$5.6, 2.9mL${\pm}$3.0 and 2.1%${\pm}$1.5 in diastolic phase, systolic phase and ejection fraction, respectively. Moreover, the presented algorithm minimizes user intervention rates which was critical to automatize algorithms in previous researches.

심장 질환을 예방하기 위해서는 정기적인 검진을 통해 심장 기능을 분석하고 관찰하는 것이 중요하다. 정기적인 검진에서 심장 기능은 심장을 촬영한 후에 관측자가 이를 수작업을 통하여 처리하여 혈류량과 심박구출률 등을 분석함으로서 이루어지나, 시간도 오래 걸리며 관측자에 따른 변이성이 문제가 된다. 본 논문에서는 심장 단축 자기공명영상에서 좌심실 영역을 분할하는 자동화된 알고리즘을 제안한다. 코일 위치에 따른 왜곡을 보정하고, K-평균 클러스터링 기법을 이용하여 좌심실 내부를 분할한다. 영상의 왜곡 및 잡음에 의하여 발생하는 분할 오류는 그래프 탐색 기법을 적용하여 수정하였다. 제안하는 알고리즘의 성능을 평가하기 위하여 38명의 지원자 그룹에 대하여 혈류량과 심박구출률을 계산하였고, 전문가에 의한 수동윤곽검출 결과와 GE MASS 소프트웨어와 비교하였다. 결과에 따르면 제안한 알고리즘의 수동윤곽검출과 혈류량의 차이는 평균적으로 이완기에 6.2mL${\pm}$5.6 및 수축기에 2.9mL${\pm}$3.0, 심박구출률의 차이는 2.1%${\pm}$1.5로 높은 정확성을 보였다. 특히 제안한 알고리즘은 기존 알고리즘에서 발생하던 사용자 간섭률을 최소화하여 자동화 성능을 향상하였다.

Keywords

References

  1. H.-Y. Lee, N. Codella, M.D. Cham, J.-W. Weinsaft, Yi Wang, "Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI," IEEE Trans. Biomedical Engineering, Vol.57(4), pp.905-913, 2010. https://doi.org/10.1109/TBME.2009.2014545
  2. N. Codella, J.-W, Weinsaft, M.-D. Cham, M Janik, M.-R. Prince and Y. Wang, "Automatic Soft Segmentation of the Left Ventricle using Myocardial Effusing Threshold Reduction and Intravoxel Computation (LV-METRIC)," Radiology, Vol.248(3), pp.1004-1012, 2008. https://doi.org/10.1148/radiol.2482072016
  3. L.P. Clarke, R.P. Velthuizel, M.A. Camacho, J.J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher and M.L. Silbige, "MRI segmentation : methods and applications," Magnetic Resonance Imaging, Vol.13(3), pp.343-368, 1995. https://doi.org/10.1016/0730-725X(94)00124-L
  4. A.E.O. Boudraa, M. Arzi, J. Sau, J.Chaimper, S. HadjMoussa, J.-E. Bessson, D. Sappey-Marinier, R. Ittiand J.-J. Mallet, "Autometical Detection of the left ventricular region in grated nuclear cardiac imaging," IEEE Trans. Biomedical Engineering, Vol.43(4), pp.430-437, 1996. https://doi.org/10.1109/10.486264
  5. A.E.O. Boudraa, "Autometical Detection of the left ventricular region in magnetic resonance images by Fuzzy C-Means model," The International Journal of Cardiac Imaging, Vol.13(4), pp.347-355, 1997. https://doi.org/10.1023/A:1005755819752
  6. J.S. Suri, "Computer vision pattern recognition and image processing in left ventricle segmentation: the last 50 years," Pattern Analysis and Applications, Vol.3, pp.209-242, 2000. https://doi.org/10.1007/s100440070008
  7. M.-P. Jolly, "Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images," International Journal of Computer Vision, Vol.70(2), pp.151-163, 2006. https://doi.org/10.1007/s11263-006-7936-3
  8. 이해연 "심장자기공명영상의 에지 분류 및 영역 확장 기법을 통한 자동 좌심실 분할 알고리즘," 정보처리학회논문지 제15-B권 제6호, pp.507-516, 2008.
  9. A. Pednekar, U. Kurkure, R. Muthupillai, S. Flamm, and I.A. Kakadiaris, "Automated left ventricular segmentation in cardiac MRI," IEEE Trans. Biomedical Engineering, Vol.53(7), pp.1425-1428, 2006. https://doi.org/10.1109/TBME.2006.873684
  10. J. Zhu-Jacquot and R. Zabih, "Segmentation of the left ventricle in cardiac MR images using graph cuts with parametric shape priors," Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp.521-524, 2008. https://doi.org/10.1109/ICASSP.2008.4517661
  11. U. Kurkure, A. Pednekar, R. Muthupillai, S.D. Flamm, I.A. and Kakadiaris, "Localization and Segmentation of Left Ventricle in Cardiac Cine-MR Images," IEEE Trans. Biomedical Engineering, Vol.56(5), pp.1360-1370, 2009. https://doi.org/10.1109/TBME.2008.2005957
  12. A. Besbes, N. Komodakis, and N. Paragios, "Graph-based knowledge-driven discrete segmentation of the left ventricle," Proc. of the IEEE Symposium on Biomedical Imaging, pp.49-52, 2009. https://doi.org/10.1109/ISBI.2009.5192980
  13. V. Torrealba, A. Bosnjak, M. Acuna, B. Solaiman, G. Montilla, and C. Roux, "Quantitative analysis of left ventricle shape and motion, using a 3D dynamic model: deformable superquadrics," Proc. of the IEEE Conf. on Engineering in Medicine and Biology Society, Vol.3, pp.1639-1641, 2000. https://doi.org/10.1109/IEMBS.2000.900390
  14. 조익환, 오정수, 엄경식, 송인찬, 장기현 및 정동석, "자기공명심장 영상의 좌심실 경계추출에서의 k 평균 군집화와 병합 알고리즘의 사용으로 인한 전처리 효과," 의공학회지 Vol.24, No.2, pp.55-60, 2003.
  15. R. El Berbari, I. Bloch, A. Redheuil, E. Angelini, E. Mousseaux, F. Frouin, and A. Herment, "An automated myocardial segmentation in cardiac MRI," Proc. of the IEEE Int. Conf. on Engineering in Medicine and Biology Society, pp.4508-4511, 2007. https://doi.org/10.1109/IEMBS.2007.4353341
  16. J. Liang, G. Ding, and Y. Wu, "Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake," Proc. of Int. Conf. on Biomedical Engineering and Informatics, Vol.2, pp.238-242, 2008. https://doi.org/10.1109/BMEI.2008.188
  17. Y. Wu, Y. Wang, and K. Lu, "Snake Model-Based Automatic Segmentation of the Left Ventricle from Cardiac MR Images," Proc. of Int. Conf. on Biomedical Engineering and Informatics, pp.1-5, 2009. https://doi.org/10.1109/BMEI.2009.5305142
  18. N. Paragios, "A level set approach for shape-driven segmentation and tracking of the left ventricle," IEEE Trans. Medical Imaging, Vol.22(6), pp.773-776, 2003. https://doi.org/10.1109/TMI.2003.814785
  19. Q. Chen, Z.M. Zhou, M. Tang, P.A. Heng, and D.-S. Xia, "Shape Statistics Variational Approach for the Outer Contour Segmentation of Left Ventricle MR Images," IEEE Trans. Information Technology in Biomedicine, Vol.10(3), pp.588-597, 2006. https://doi.org/10.1109/TITB.2006.872051
  20. S. Choi, H. Kim, J. Oh, T. Kang, K. Sun, and M. Kim, "Segmentation of the left ventricle in myocardial perfusion SPECT using variational level set formulation," Proc. of the IEEE Nuclear Science Symposium Conference Record, Vol.4, pp.3060-3064, 2007. https://doi.org/10.1109/NSSMIC.2007.4436776