Pattern Analysis and Applications

, Volume 9, Issue 4, pp 293–306 | Cite as

A semi-automatic system for segmentation of cardiac M-mode images

  • Luca Bertelli
  • Rita Cucchiara
  • Giovanni Paternostro
  • Andrea Prati
Theoretical Advances


Pixel classifiers are often adopted in pattern recognition as a suitable method for image segmentation. A common approach to the performance evaluation of classifier systems is based on the measurement of the classification errors and, at the same time, on the computational time. In general, multiclassifiers have proven to be more precise in the classification in many applications, but at the cost of a higher computational load. This paper analyzes different classifiers and proposes an evaluation of the classifiers in the case of semi-automatic processes with human interaction. Medical imaging is a typical application, where automatic or semi-automatic segmentation can be a valuable support to the diagnosis. The paper focuses on the segmentation of cardiac images of fruit flies (genetic model for analyzing human heart’s diseases). Analysis is based on M-modes, that are gray-level images derived from mono-dimensional projections of the video frames on a line. Segmentation of the M-mode images is provided by classifiers and integrated in a multiclassifier. A neural network classifier, a Bayesian classifier, and a classifier based on hidden Markov chains are joined by means of a Behavior Knowledge Space fusion rule. The comparative evaluation is discussed in terms of both accuracy and required time, in which the time to correct the classifier errors by means of human intervention is also taken into account.


Performance evaluation Neural network Hidden Markov chains Bayesian classifiers Multiclassifier Image segmentation Cardiac imaging 


  1. 1.
    Paternostro G, Vignola C, Bartsch DU, Omens JH, McCulloch AD, Reed JC (2001) Age-associated cardiac dysfunction in Drosophila melanogaster. Circ Res 88:1053–1058Google Scholar
  2. 2.
    Lakatta EG (2001) Heart aging: a fly in the ointment? Circ Res 88:984–986Google Scholar
  3. 3.
    Frangi AF, Niessen WJ, Viergever MA (2001) Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Trans Med Imaging 20(1):2–25CrossRefGoogle Scholar
  4. 4.
    McEachen JK II, Duncan JS (1997) Shape-based tracking of left ventricular wall motion. IEEE Trans Med Imaging 26(3):270–283CrossRefGoogle Scholar
  5. 5.
    McVeigh ER (1996) MRI of myocardial function: motion tracking techniques. Magn Reson Imaging 14(2):137–150CrossRefGoogle Scholar
  6. 6.
    Nahrendorf M, Hiller K-H, Hu K, Ertl G, Haase A, Bauer WR (2003) Cardiac magnetic resonance imaging in small animal models of human heart failure. Med Image Anal 7(3):369–375CrossRefGoogle Scholar
  7. 7.
    Waiter GD, McKiddie FI, Redpath TW, Semple SIK, Trent RJ (1999) Determination of normal regional left ventricular function from cine-MR images using a semi-automated edge detection method. Magn Reson Imaging 17(1):99–107CrossRefGoogle Scholar
  8. 8.
    Amini AA, Yasheng C, 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
  9. 9.
    Denney TS Jr (1999) Estimation and detection of myocardial tags in MR image without user-defined myocardial contours. IEEE Trans Med Imaging 18(4):330–344CrossRefGoogle Scholar
  10. 10.
    Haber I, Metaxas DN, Axel L (2000) Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI. Med Image Anal 4(4):335–355CrossRefGoogle Scholar
  11. 11.
    Osman NF, McVeigh ER, Prince JL (2000) Imaging heart motion using harmonic phase MRI. IEEE Trans Med Imaging 19(3):186–202CrossRefGoogle Scholar
  12. 12.
    Klein GJ, Huesman RH (2002) Four-dimensional processing of deformable cardiac PET data. Med Image Anal 6(1):29–46CrossRefGoogle Scholar
  13. 13.
    Debreuve E, Barlaud M, Aubert G, Laurette I, Darcourt J (2001) Space–time segmentation using level set active contours applied to myocardial gated SPECT. IEEE Trans Med Imaging 20(7):643–659CrossRefGoogle Scholar
  14. 14.
    Bosch JG, Mitchell SC, Lelieveldt BPF, Nijland F, Kamp O, Sonka M, Reiber JHC (2002) Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Trans Med Imaging 21(11):1374–1383CrossRefGoogle Scholar
  15. 15.
    Gerard O, Billon AC, Rouet JM, Jacob M, Fradkin M, Allouche C (2002) Efficient model-based quantification of left ventricular function in 3-D echocardiography. IEEE Trans Med Imaging 21(9):1059–1068CrossRefGoogle Scholar
  16. 16.
    Jacob G, Noble JA, Behrenbruch C, Kelion AD, Banning AP (2002) A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography. IEEE Trans Med Imaging 21(3):226–238CrossRefGoogle Scholar
  17. 17.
    Montagnat J, Sermesant M, Delingette H, Malandain G, Ayache N (2003) Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images, Pattern Recognit Lett 24(4–5):815–828CrossRefGoogle Scholar
  18. 18.
    Clarysse P, Friboulet D, Magnin IE (1997) Tracking geometrical descriptors on 3-D deformable surfaces: application to the left-ventricular surface of the heart. IEEE Trans Med Imaging 16(4):392–404CrossRefGoogle Scholar
  19. 19.
    Herment A, Mousseaux E, Dumée P, Decesare A (1998) Automatic detection of left ventricular borders on electron beam CT sequential cardiac images using an adaptive algorithm, Comput Med Imaging Graph 22(4):291–299CrossRefGoogle Scholar
  20. 20.
    Kass M, Witkin A, Terzopolous D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331CrossRefGoogle Scholar
  21. 21.
    Staib LH, Duncan JS (1992) Boundary finding with parametrically deformable models. IEEE Trans Patt Anal Mach Intell 14(11):1061–1075CrossRefGoogle Scholar
  22. 22.
    Chalana V, Linker DT, Haynor DR, Yongmin K (1996) A multiple active contour model for cardiac boundary detection on echocardiographic sequences. IEEE Trans Med Imaging 15(3):290–298CrossRefGoogle Scholar
  23. 23.
    Yuille AL, Hallinan P (1992) Deformable templates. In: Blake A, Yuille AL (eds) Active vision. MIT press, Cambridge, pp 20–38Google Scholar
  24. 24.
    Cootes TF, Taylor CJ, Cooper DH, Graham P (1992) Training models of shape from sets of examples. In: Proceedings of British machine vision conference, pp 9–18Google Scholar
  25. 25.
    García-Fernández MA, Zamorano JL, Azevedo J (1997) Doppler tissue imaging. McGraw-Hill, New YorkGoogle Scholar
  26. 26.
    Malpica N, Santos A, Pérez E, García-Fernández MA, Desco M (2003) A snake model for anatomic M-mode tracking in echocardiography. In: Loncaric S, Neri A, Babic H (eds) Proceedings of 3rd international symposium on image and signal processing and analysis 2, pp 722–726Google Scholar
  27. 27.
    Mele D, Pedini I, Alboni P, Levine RA (1998) Anatomic M-mode: a new technique for quantitative assessment of left ventricular size and function. Am J Cardiol 81:82G–85GCrossRefGoogle Scholar
  28. 28.
    Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comp Vis Graph Image Process 29:100–132CrossRefGoogle Scholar
  29. 29.
    Pal NR, Pal SK (1993) A review of image segmentation techniques. Pattern Recognit 26(9):1277–1294MathSciNetCrossRefGoogle Scholar
  30. 30.
    Tobias OJ, Seara R (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11(12):1457–1465CrossRefGoogle Scholar
  31. 31.
    Manay S, Yezzi A (2003) Anti-geometric diffusion for adaptive thresholding and fast segmentation. IEEE Trans Image Process 12(11):1310–1323MathSciNetCrossRefGoogle Scholar
  32. 32.
    Roli F, Giacinto G (2002) Design of multiple classifier systems. In: Bunke H, Kandel A (eds) Hybrid methods in pattern recognition. World Scientific Publishing, SingaporeGoogle Scholar
  33. 33.
    Kittler J, Hater M, Duin RPW (1996) On combining classifiers. In: Proceedings of 13th international conference on pattern recognition, vol 2, pp 897–901Google Scholar
  34. 34.
    Woods K, Kegelmeyer WP Jr, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19(4):405–410CrossRefGoogle Scholar
  35. 35.
    Plessis B, Sicsu A, Heutte L, Menu E, Lecolinet E, Debon O, Moreau J-V (1993) A multi-classifier combination strategy for the recognition of handwritten cursive words. In: Proceedings of 2nd international conference on document analysis and recognition, pp 642–645Google Scholar
  36. 36.
    Dasarathy BV, McCullough CL (1998) Intelligent multi-classifier fusion for decision making in ballistic missile defense applications. In: Proceedings of IEEE conference on decision and control, vol 1, pp 233–238Google Scholar
  37. 37.
    Briem GJ, Benediktsson JA, Sveinsson JR (2001) Boosting, bagging, and consensus based classification of multisource remote sensing data. In: Josef Kittler, Fabio Roli (eds) Proceedings of 2nd international workshop on multiple classifier systems (LNCS 2096). Springer, Berlin Heidelberg New York, pp 279–288Google Scholar
  38. 38.
    Jain AK, Prabhakar S, Hong L (1999) A multichannel approach to fingerprint classification. IEEE Trans Pattern Anal Mach Intell 21(4):348–358CrossRefGoogle Scholar
  39. 39.
    Chiou GI, Hwang J-N (1994) Image sequence classification using a neural network based active contour model and a hidden Markov model. In: Proceedings of international conference on image processing, vol 3, pp 926–930Google Scholar
  40. 40.
    Shah S, Sastry PS (2004) Fingerprint classification using a feedback-based line detector. IEEE Trans Syst Man Cybern B 34(1):85–94CrossRefGoogle Scholar
  41. 41.
    Zhang X-P (2001) Thresholding neural network for adaptive noise reduction. IEEE Trans Neural Netw 12(3):567–584CrossRefGoogle Scholar
  42. 42.
    Komo D, Chang C-I, Ko H (1994) Neural network technology for stock market index prediction. In: Proceedings of international symposium on speech, image processing and neural networks, vol 2, pp 543–546Google Scholar
  43. 43.
    Kuntimad G, Ranganath HS (1999) Perfect image segmentation using pulse coupled neural networks. IEEE Trans Neural Netw 10(3):591–598CrossRefGoogle Scholar
  44. 44.
    Rae R, Ritter HJ (1998) Recognition of human head orientation based on artificial neural networks. IEEE Trans Neural Netw 9(2):257–265CrossRefGoogle Scholar
  45. 45.
    Anthony M, Bartlett PL (1999) Neural network learning: theoretical foundation. Cambridge University Press, CambridgeGoogle Scholar
  46. 46.
    Lei T, Udupa JK (2003) Performance evaluation of finite normal mixture model-based image segmentation techniques. IEEE Trans Image Process 12(10):1153–1169CrossRefGoogle Scholar
  47. 47.
    Acton ST, Mukherjee DP (2000) Scale space classification using area morphology. IEEE Trans Image Process 9(4):623–635CrossRefGoogle Scholar
  48. 48.
    Giordana N, Pieczynski W (1997) Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation. IEEE Trans Pattern Anal Mach Intell 19(5):465–475CrossRefGoogle Scholar
  49. 49.
    Pieczynski W (2003) Pairwise Markow chains. IEEE Trans Pattern Anal Mach Intell 25(5):634–639CrossRefGoogle Scholar
  50. 50.
    Marroquin JL, Santana EA, Botello S (2003) Hidden Markov measure field models for image segmentation. IEEE Trans Pattern Anal Mach Intell 25(11):1380–1387CrossRefGoogle Scholar
  51. 51.
    Cai J, Liu Z-Q (2001) Hidden Markov models with spectral features for 2D shape recognition. IEEE Trans Pattern Anal Mach Intell 23(12):1454–1458CrossRefGoogle Scholar
  52. 52.
    Chien J-T (1999) Online hierarchical transformation of hidden Markov models for speech recognition. IEEE Trans Speech Audio Proc 7(6):656–667CrossRefGoogle Scholar
  53. 53.
    Bao P, Zhang L (2003) Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Trans Med Imaging 22(9):1089–1099CrossRefGoogle Scholar
  54. 54.
    Fjortoft R, Delignon Y, Pieczynski W, Sigelle M, Tupin F (2003) Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. IEEE Trans Geos Rem Sensing 41(3):675–686CrossRefGoogle Scholar
  55. 55.
    Ephraim Y, Merhav N (2002) Hidden Markov processes. IEEE Trans Inform Theory 48(6):1518–1569zbMATHMathSciNetCrossRefGoogle Scholar
  56. 56.
    Forney GD Jr (1973) The Viterbi algorithm. Proc IEEE 61:268–278MathSciNetCrossRefGoogle Scholar
  57. 57.
    Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inform Theory IT-13:260–269CrossRefGoogle Scholar
  58. 58.
    Xu L, Krzyzak A, Suen CY (1992) Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern 22(3):418–435CrossRefGoogle Scholar
  59. 59.
    Huang YS, Suen CY (1995) A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans Pattern Anal Mach Intell 17(1):90–94CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Luca Bertelli
    • 1
    • 2
  • Rita Cucchiara
    • 2
  • Giovanni Paternostro
    • 1
  • Andrea Prati
    • 3
  1. 1.The Burnham InstituteLa JollaUSA
  2. 2.Dipartimento di Ingegneria dell’InformazioneUniversity of Modena and Reggio EmiliaModenaItaly
  3. 3.Dipartimento di Scienze e Metodi dell’IngegneriaUniversity of Modena and Reggio EmiliaReggio EmiliaItaly

Personalised recommendations