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A framework for automatic analysis of the dynamic behaviour of coronary angiograms

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Abstract

A framework for coronary vessels analysis in digital subtracted angiograms is described. This method combines the motion estimation with the frame-to-frame structure detection in a natural way such that they act interactively. The first step consists of the extraction of the vessel centrelines in one image and their organization into meaningful constituents or branches of the coronary arterial tree. The motion is then estimated along the centrelines through a gradient based method. These motion estimates supply an initial positioning of an active contour model (or ‘snake“) in the next image. This model adapts itself by changing its shape to accurately fit onto the new centrelines. This process is then reiterated on the subsequent images to depict the dynamic behaviour of all the relevant branches. The main interests of this scheme are: (1) the active models operate locally so a fast detection of the vessels can be performed; (2) the centrelines extraction is fully guided by the confluence of the motion estimation and the contour model; (3) both morphological and kinetic features are provided on a quantitative basis.

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References

  1. Rushmer RF, Crystal DK, Wagner C. The functional anatomy of left ventricular contraction. Circ Res 1953; 1: 162–70.

    Google Scholar 

  2. Garrison JB, Ebert WL, Jenkins RE, Yiionoulis SM, Malcom H, Heyler GA, et al. Measurement of three-dimensional positions and motions of large numbers of spherical radiopaque markers from biplane cineradiograms. Comput Biomed Res 1982; 15: 76–96.

    Google Scholar 

  3. Tsotsos JK. Knowledge organisation and its role in representation and interpretation for time-varying data: the AL-VEN system. Comput Intel 1985; 1: 16–32.

    Google Scholar 

  4. Kong Y, Morris JJ, Mc Intosh HD. Assessment of regional myocardial performance from bi-plane coronary cine angiograms. Am Cardiol 1971; 27: 529–37.

    Google Scholar 

  5. Potel MJ, Rubin JM, Mackay SA, Aisen AM, Al-Sadir J, Sayre RE. Methods for evaluating cardiac wall motion in three dimensions using bifurcation points of the coronary arterial tree. Invest Radiol 1983; 18: 47–57.

    Google Scholar 

  6. Stevenson DJ, Smith LDR, Robinson G. Working towards the automatic detection of blood vessels in X-ray angiograms. Pattern Recog Letters 1987; 6: 107–12.

    Google Scholar 

  7. Mailloux GE, Bleau A, Bertrand M, Petitclerc R. Computer analysis of heart motion from two-dimensional echocardiograms. IEEE Trans Biomed Eng 1987; 34(5): 356–64.

    Google Scholar 

  8. Horn BKP, Schunck BG. Determining optical flow. Artif Intell 1981; 17: 185–203.

    Google Scholar 

  9. Cornelius N, Kanade T. Adapting optical flow to measure object motion in reflectance and X-ray image sequences. In: Siggraph/Sigart. Interdisciplinary Workshop. Motion: Representation and perception. Toronto (Canada), 1983: 50–8.

  10. Rong JH, Collorec R, Coatrieux JL, Descaves C. Estimation de mouvement en coronarographie. Innov Technol Biol Med 1989; 10(2): 175–86.

    Google Scholar 

  11. Hoffmann KR, Doi K, Chan HP, Fencil L, Fujita H, Muraki A. Automated tracking of the vascular tree in DSA images using a double-square-box region-of-search algorithm. SPIE: Society of Photo-optical Instrumentation Engineers, 626 Medicine XIV 1986, PACS IV: 326–32.

    Google Scholar 

  12. Nguyen TV, Sklansky J. A fast skeleton-finder for coronary arteries. In: Proceedings 8th Int. Conf. Pattern Recognition. Paris, 1986; 1: 481–3.

    Google Scholar 

  13. Tsuji S, Nakano H. Knowledge-based identification of artery branches in cine-angiograms. In: Proceedings 7th IJ-CAI. 1981: 710–5.

  14. Reiber JHC, Serruys PW, Slager CJ. Structural analysis of the coronary and retinal tree. In: Quantitative coronary and left ventricular cineangiography. Martinus Nijhoff Editor, 1986: 185–213.

  15. Walker DR, Rao KR. Improved pel-recursive motion compensation. IEEE Trans Commun 1984; 32(10): 1128–34.

    Google Scholar 

  16. Thompson WB, Barnard ST. Lower-level estimation and interpretation of visual motion. Comput 1981; 14(8): 20–8.

    Google Scholar 

  17. Nagel HH, Enkelmann W. An investigation of smoothness constraints for estimation of displacement vector fields from image sequences. IEEE Trans Pattern Anal Machine Intell 1986; 8(5): 565–93.

    Google Scholar 

  18. Nagel HH. From image sequences towards conceptual descriptions. Image vision comput 1988; 6(2): 59–74.

    Google Scholar 

  19. Thompson WB. Combining motion and contrast for segmentation. IEEE Trans Pattern Anal Machine Intell 1980;2(6): 543–9.

    Google Scholar 

  20. Hildreth EC. Computations underlying the measurement of visual motion. Artif Intell 1984; 23: 309–54.

    Google Scholar 

  21. Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vision 1988; 1(4): 321–31.

    Google Scholar 

  22. Stansfield SA. ANGY: A rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms. IEEE Trans Patter Anal Machine Intell 1986; 8(2): 188–99.

    Google Scholar 

  23. Toumoulin C. Traitement d'images multimodalité dans un réseau d'imagerie médicale. Application à la segmentation d'images de radiologie numérique et de résonance magnétique [Thesis] Rennes (F): Univ of Rennes I, 1988.

    Google Scholar 

  24. Collorec R, Coatrieux JL. Vectorial tracking and directed contour finder for vascular network in digital subtraction angiography. Pattern Recognition Letters 1988; 8: 353–8.

    Google Scholar 

  25. Toumoulin C, Collorec R, Coatrieux JL. Vascular network segmentation in subtraction angiograms: a comparative study. Med Inf 1990; 15(4): 333–41.

    Google Scholar 

  26. Van Meenen RJ. Detection of the coronary tree in series of digital, subtraction images [Thesis]. Delft (NL): Information Theory Group, Univ of Technology, 1985.

    Google Scholar 

  27. Rong JH, Collorec R, Coatrieux JL. Model guided automatic frame-to-frame segmentation in digital subtraction angiography. Proc SPIE 1989; 1137.

  28. Mittiche A, Wang YF, Aggarwal JK. Experiments in computing optical flow with the gradient-based, multiconstraint method. Pattern Recog 1987; 20(2): 173–9.

    Google Scholar 

  29. Haralick RM, Lee JS. The facet approach to optic flow. In: Baumann LS, (ed.). Proceedings image understanding workshop Arlington (VA): Science Applications, 1983: 84–93.

    Google Scholar 

  30. Tretiak O, Pastor L. Velocity estimation from image sequences with second order differential operators. In: Proceedings Int Conf Pattern Recognition Montréal, 1984: 16–9.

  31. Nagel HH. On the estimation of optical flow: Relations between different approaches and some new results. Artif Intell 1987; 33: 299–324.

    Google Scholar 

  32. Rong J. Estimation et caractérisation du mouvement en coronarographie [Thesis]. Rennes (F): Univ of Rennes I, 1989.

    Google Scholar 

  33. Carreau M, Coatrieux JL, Collorec R, Chardenon C. A knowledge based approach for 3D reconstruction and labeling of vascular network from biplane angiographic projections. IEEE Trans Med Imag 1991; 10(2): 122–31.

    Google Scholar 

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Coatrieux, J.L., Rong, J. & Collorec, R. A framework for automatic analysis of the dynamic behaviour of coronary angiograms. Int J Cardiac Imag 8, 1–10 (1992). https://doi.org/10.1007/BF01137561

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