pp 1–13 | Cite as

A novel machine-learning algorithm to estimate the position and size of myocardial infarction for MRI sequence

  • Chenchu Xu
  • Lin Xu
  • Heye ZhangEmail author
  • Yanping Zhang
  • Xiuquan Du
  • Shuo Li


Early and accurate assessment of myocardial abnormalities is of utmost importance in the diagnosis of myocardial infarction (MI). In this study, we proposed a machine-learning and image motion statistic based approach to automating the detection and localization of MI area in magnetic resonance images. Unlike the existing techniques, the proposed method that could be directly acquired position and size of MI area with sub-pixel precision. Standard clinical magnetic resonance image and delayed enhancement imaging data of 58 patients with MI were used for developing this algorithm. First, we are located and extracted the LV from the original MR image. Then, we using a novel Optical Flow algorithm to building statistical image motion features include the motion trajectories of each Point on myocardium in whole frames. In the end, we using these results as inputs to a support vector machine classifier, which can obtain an MI area assessment of the myocardium. Compared to the pixel by pixel in delayed enhancement imaging, the proposed algorithm yielded the highest classification accuracy of 93.34% and the kappa measure of 0.74. The field experiments our method performs significantly better than other recent methods, and can lead to a promising diagnostic support tool to assists clinicians, particularly for novice readers with limited experience.


Myocardial infarction Optical flow Motion feature 

Mathematics Subject Classification




Funding was provided by National Key Research and Development Program of China (Grant No. 2016YFC1300300), The Natural Science Foundation of Jiangsu Province of China (Grant BK20150931). National Natural Science Foundation of China (Grant Nos. 61771464, U1801265, 61702275, 41775008, 61673020), Shen Zhen Research and Innovation Funding (Grant No. JCYJ20170307165309009), Provincial Natural Science Research Program of the Higher Education Institutions of Anhui Province (Grant No. KJ2016A016).


  1. 1.
    Afshin M, Ayed IB, Punithakumar K, Law MW, Islam A, Goela A, Peters TM, Li S (2014) Regional assessment of cardiac left ventricular myocardial function via MRI statistical features. IEEE Trans Med Imaging 33(2):481–494CrossRefGoogle Scholar
  2. 2.
    Amini AA, Chen Y, Elayyadi M, Radeva P (2001) Tag surface reconstruction and tracking of myocardial beads from SPAMM-MRI with parametric B-spline surfaces. IEEE Trans Med imaging 20(2):94–103CrossRefGoogle Scholar
  3. 3.
    Bai W, Peressutti D, Oktay O, Shi W, O’egan DP, King AP, Rueckert D (2015) Learning a global descriptor of cardiac motion from a large cohort of 1000+ normal subjects. In: International conference on functional imaging and modeling of the heart. Springer, Berlin, pp 3–11CrossRefGoogle Scholar
  4. 4.
    Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77CrossRefGoogle Scholar
  5. 5.
    Bijnens B, Claus P, Weidemann F, Strotmann J, Sutherland GR (2007) Investigating cardiac function using motion and deformation analysis in the setting of coronary artery disease. Circulation 116(21):2453–2464CrossRefGoogle Scholar
  6. 6.
    Bosch JG, Nijland F, Mitchell SC, Lelieveldt BP, Kamp O, Reiber JH, Sonka M (2005) Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms1. Acad Radiol 12(3):358–367CrossRefGoogle Scholar
  7. 7.
    Burton A, Radford J (1978) Thinking in perspective: critical essays in the study of thought processes, vol 646. Routledge, AbingdonGoogle Scholar
  8. 8.
    Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  9. 9.
    Charles R (2000) Applying computational mechanics analysis to biological systems. In: 18th applied aerodynamics conference, p 4338Google Scholar
  10. 10.
    Chéhikian A (1992) Optimal algorithms for low-pass and laplacian image pyramids computation. Traitement du Signal 9:297–297Google Scholar
  11. 11.
    Eichstaedt H, Felix R, Dougherty F, Langer M, Rutsch W, Schmutzler H (1986) Magnetic resonance imaging (MRI) in different stages of myocardial infarction using the contrast agent gadolinium-dtpa. Clin Cardiol 9(11):527–535CrossRefGoogle Scholar
  12. 12.
    Frangl A, Rueckert D, Duncan JS (2002) Three-dimensional cardiovascular image analysis. IEEE Trans Med Imaging 21(9):1005–1010CrossRefGoogle Scholar
  13. 13.
    Garcia-Barnes J, Gil D, Badiella L, Hernandez-Sabate A, Carreras F, Pujades S, Marti E (2010) A normalized framework for the design of feature spaces assessing the left ventricular function. IEEE Trans Med Imaging 29(3):733–745CrossRefGoogle Scholar
  14. 14.
    Hoffmann R, von Bardeleben S, Kasprzak JD, Borges AC, ten Cate F, Firschke C, Lafitte S, Al-Saadi N, Kuntz-Hehner S, Horstick G et al (2006) Analysis of regional left ventricular function by cineventriculography, cardiac magnetic resonance imaging, and unenhanced and contrast-enhanced echocardiography: a multicenter comparison of methods. J Am Coll Cardiol 47(1):121–128CrossRefGoogle Scholar
  15. 15.
    Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203CrossRefGoogle Scholar
  16. 16.
    Hsu C-W, Chang C-C, Lin C-J (2008) A practical guide to support vector classification. BJU Int 101:1396–1400CrossRefGoogle Scholar
  17. 17.
    Jolly MP (2008) Automatic recovery of the left ventricular blood pool in cardiac cine MR images. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 110–118CrossRefGoogle Scholar
  18. 18.
    Klein C, Nekolla SG, Bengel FM, Momose M, Sammer A, Haas F, Schnackenburg B, Delius W, Mudra H, Wolfram D et al (2002) Assessment of myocardial viability with contrast-enhanced magnetic resonance imaging: comparison with positron emission tomography. Circulation 105(2):162–167CrossRefGoogle Scholar
  19. 19.
    Lei C, Yang YH (2009) Optical flow estimation on coarse-to-fine region-trees using discrete optimization. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 1562–1569Google Scholar
  20. 20.
    Leung KE, Bosch JG (2007) Localized shape variations for classifying wall motion in echocardiograms. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 52–59Google Scholar
  21. 21.
    Lipton MJ, Bogaert J, Boxt LM, Reba RC (2002) Imaging of ischemic heart disease. Eur Radiol 12(5):1061–1080CrossRefGoogle Scholar
  22. 22.
    Mansor S, Noble JA (2008) Local wall motion classification of stress echocardiography using a hidden markov model approach. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, 2008. ISBI 2008. IEEE, pp 1295–1298Google Scholar
  23. 23.
    örg Barkhausen J, Ebert W, Weinmann HJ et al (2002) Imaging of myocardial infarction: comparison of magnevist and gadophrin-3 in rabbits. J Am Coll Cardiol 39(8):1392–1398CrossRefGoogle Scholar
  24. 24.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet 9(1):62–66CrossRefGoogle Scholar
  25. 25.
    Petitjean C, Dacher JN (2011) A review of segmentation methods in short axis cardiac MR images. Med Image Anal 15(2):169–184CrossRefGoogle Scholar
  26. 26.
    Qian Z, Liu Q, Metaxas DN, Axel L (2011) Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis. IEEE Trans Med Imaging 30(12):2017–2029CrossRefGoogle Scholar
  27. 27.
    Shi P, Liu H (2003) Stochastic finite element framework for simultaneous estimation of cardiac kinematic functions and material parameters. Med Image Anal 7(4):445–464CrossRefGoogle Scholar
  28. 28.
    Suinesiaputra A, Ablin P, Alba X et al (2018) Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE J Biomed Health Inform 22(2):503–515CrossRefGoogle Scholar
  29. 29.
    Suinesiaputra A, Frangi AF, Kaandorp TA, Lamb HJ, Bax JJ, Reiber JH, Lelieveldt BP (2009) Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans Med Imaging 28(4):595–607CrossRefGoogle Scholar
  30. 30.
    Viera AJ, Garrett JM et al (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37(5):360–363Google Scholar
  31. 31.
    Wang H, Amini AA (2012) Cardiac motion and deformation recovery from MRI: a review. IEEE Trans Med Imaging 31(2):487–503CrossRefGoogle Scholar
  32. 32.
    Xavier M, Lalande A, Walker PM, Brunotte F, Legrand L (2012) An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard cine-MR examinations. IEEE Trans Inf Technol Biomed 16(5):859–868CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenZhenChina
  2. 2.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  3. 3.Department of CardiologyGuangzhou General Hospital of Guangzhou Military Region, PLAGuangzhouChina
  4. 4.Department of Medical ImagingWestern UniversityLondonCanada

Personalised recommendations