Left Ventricle Segmentation Algorithm through Radial Threshold Determination on Cardiac MRI

심장 자기공명영상에서 방사형 임계치 결정법을 통한 좌심실 분할 알고리즘

  • 문창배 (금오공과대학교 컴퓨터공학부) ;
  • 이해연 (금오공과대학교 컴퓨터공학부) ;
  • 김병만 (금오공과대학교 컴퓨터공학부) ;
  • 신윤식 (금오공과대학교 컴퓨터공학부)
  • Published : 2009.10.15

Abstract

The advance in medical technology has decreased death rates from diseases such as tubercle, pneumonia, malnutrition, and hepatitis. However, death rates from cardiac diseases are still increasing. To prevent cardiac diseases and quantify cardiac function, magnetic resonance imaging not harmful to the body is used for calculating blood volumes and ejection fraction(EF) on routine clinics. In this paper, automatic left ventricle(LV) segmentation is presented to segment LV and calculate blood volume and EF, which can replace labor intensive and time consuming manual contouring. Radial threshold determination is designed to segment LV and blood volume and EF are calculated. Especially, basal slices which were difficult to segment in previous researches are segmented automatically almost without user intervention. On short axis cardiac MRI of 36 subjects, the presented algorithm is compared with manual contouring and General Electronic MASS software. The results show that the presented algorithm performs in similar to the manual contouring and outperforms the MASS software in accuracy.

의학기술이 발전하면서 결핵, 폐렴, 영양실조, A형간염 등의 질병에 의한 사망률은 감소하는 반면, 심장 질환으로 인한 사망률은 증가하는 추세이다. 심장병을 예방하기 위하여 정기적인 검사가 중요하고, 인체에 무해한 자기공명영상을 활용하여 심장의 혈류량과 심박구출률을 계산하여 심장의 기능을 분석할 필요가 있다. 본 논문에서는 기존의 노동집약적이고 시간적 비용이 큰 수동윤곽분할을 대체하기 위한 자동 좌심실 분할 알고리즘을 제안하였다. 방사형 임계치 결정법을 통하여 심실을 분할하고 혈류량 및 심박구출률을 계산하였으며, 특히 기존 방법들에서 문제가 되었던 기저 영상도 사용자 간섭률을 최소화하여 자동분할을 수행하였다. 제안 알고리즘의 검증을 위하여 36명의 심장 자기공명영상 데이터를 사용하여 전문가에 의한 수동윤곽분할 및 제너럴일렉트로닉스 MASS 소프트웨어와 정량적 비교를 수행하였다. 실험을 통해 제안한 방법이 표준으로 간주되는 수동윤곽분할과 정확도가 유사하며, MASS 소프트웨어보다 높은 정확도를 갖고 있음을 알 수 있었다.

Keywords

References

  1. H.-Y. Lee, N. Codella, M. Cham, J. Weinsaft, and Y. Wang, 'Automatic Left Ventricle Segmentation using Iterative Thresholding and Active Contour Model with Adaptation on Short-Axis Cardiac MRI,' IEEE Trans. on Biomedical Engineering, 2009. (in press)
  2. H.-Y. Lee, N. Codella, M. Cham, M. Prince, J. Weinsaft, and Y. Wang, 'Left ventricle segmentation using Graph searching on Intensity and Gradient and A priori knowledge (lvGIGA) for short axis cardiac MRI,' Journal of Magnetic Resonance Imaging, vol.28, 2008, pp.1393-1401 https://doi.org/10.1002/jmri.21586
  3. J.S. Suri, 'Computer vision pattern recognition and image processing in left ventricle segmentation: the last 50 years,' Pattern Analysis and Applications, vol.3, 2000, pp.209-242 https://doi.org/10.1007/s100440070008
  4. A.S. Pednekar, R. Muthupillai, V.V. Lenge, I.A. Kakadiaris, and S.D. Flamm, 'Automatic identification of the left ventricle in cardiac cine-MR images: dual-contrast cluster analysis and scoutgeometry approaches,' Journal of Magnetic Resonance Imaging, vol.23(5), 2006, pp.641-651 https://doi.org/10.1002/jmri.20552
  5. M. Lynch, O. Ghita, and P.F. Whelan, 'Leftventricle myocardium segmentation using a coupled level-set with a priori knowledge,' Computerized Medical Imaging and Graphics, vol.30, 2006, pp. 255-262 https://doi.org/10.1016/j.compmedimag.2006.03.009
  6. Z. Zhou, J. You, P.A. Heng, and D. Xia, 'Cardiac MR image segmentation and left ventricle surface reconstruction based on level set method,' Studies in health technology and informatics, vol.111, 2005, pp.629-632
  7. N.C. Codella, J.W. Weinsaft, M.D. Cham, M. Janick, M. Prince, and Y. Wang, 'Left Ventricle: Automated Segmentation by Using Myocardial Effusion Threshold Reduction and Intravoxel Computation at MR Imaging,' Radiology, vol.248(3), 2008, pp.1004-1012 https://doi.org/10.1148/radiol.2482072016
  8. R.J. van Geuns, T. Baks, E.H. Gronenschild, J.P. Aben, P.A. Wielopolski, F. Cademartiri, and P.J. de Feyter, 'Automatic quantitative left ventricular analysis of cine MR images by using threedimensional information for contour detection,' Radiology, vol.240(1), 2006, pp.215-221 https://doi.org/10.1148/radiol.2401050471
  9. A. Pednekar, U. Kurkure, R. Muthupillari, S. Flamm, and I.A. Kakadiaris, 'Automated Left Ventricle Segmentation in Cardiac MRI,' IEEE Trans. Biomedical Engineering, vol.53(7), 2006, pp.1425-1428 https://doi.org/10.1109/TBME.2006.873684
  10. M.F. Santarelli, V. Positano, C. Michelassi, M. Lombardi, and L. Landini, 'Automated cardiac MR image segmentation: theory and measurement evaluation,' Medical Engineering & Physics, vol.25(2), 2003, pp.149-159 https://doi.org/10.1016/S1350-4533(02)00144-3
  11. M.R. Kaus, J. von Berg, J. Weese, W. Niessen, and V. Pekar, 'Automated segmentation of the left ventricle in cardiac MRI,' Medical Image Analysis, vol.8(3), 2004, pp.245-254 https://doi.org/10.1016/j.media.2004.06.015
  12. M.-P. Jolly, 'Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images,' International Journal of Computer Vision, vol.70(2), 2006, pp.151-163 https://doi.org/10.1007/s11263-006-7936-3
  13. R.J. van der Geest, B.P. Lelieveldt, E. Angelie, M. Danilouchkine, C. Swingen, M. Sonka, and J.H. Reiber, 'Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model,' Journal of Cardiovascular Magnetic Resonance, vol.6(3), 2004, pp.609-617 https://doi.org/10.1081/JCMR-120038082
  14. 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), 2006, pp. 588-597 https://doi.org/10.1109/TITB.2006.872051
  15. N. Paragios, 'A level set approach for shapedriven segmentation and tracking of the left ventricle,' IEEE Trans. Medical Imaging, vol.22(6), 2003, pp.773-776 https://doi.org/10.1109/TMI.2003.814785
  16. C. Corsi, C. Lamberti, R. Battani, A. Maggioni, G. Discenza, P. MacEneaney, V. Mor-Avi, R.M. Lang and E.G. Caiani, 'Computerized quantification of left ventricular volumes on cardiac magnetic resonance images by level set method,' Computer Assisted Radiology and Surgery, vol.1268, 2004, pp.1114-1119 https://doi.org/10.1016/j.ics.2004.03.117
  17. M. Lynch, O. Ghita, and P.F. Whelan, 'Automatic segmentation of the left ventricle cavity and myocardium in MRI data,' Computers in Biology and Medicine, vol.36(4), 2006, pp.389-407 https://doi.org/10.1016/j.compbiomed.2005.01.005