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Reconstruction of 3-D branching structures

  • 1. Image Formation And Reconstruction
  • Conference paper
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Information Processing in Medical Imaging (IPMI 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 511))

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Abstract

We describe a new approach to the problem of reconstructing 3-D branching objects from a small number of projections. Given the object-to-image transformations and an assumption of structural connectivity, we describe how to elucidate the imaged structure from the multitude of artifacts resulting from all possible triangulations. The method involves generating two or more intermediate reconstructions from different pairs of projections, then comparing structural similarities/differences to identify the artifacts. Since the intermediate reconstructions are assured to be connected (i.e. not fragmented), it becomes possible to refine portions of the structure that previously were considered ambiguous. Simulations were performed using a mathematically defined test object having 10 branches. In every case, the true structure was able to be distinguished from the artifacts using as few as three projections.

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References

  • Henri CJ, Peters TM, Lemieux L and Olivier A (1990). Experience with a computerized stereoscopic workstation for neurosurgical planning. In: Proc. First Conf. on Visualization in Biomedical Computing. IEEE Computer Society Press, Los Alamitos CA, pp. 450–457.

    Google Scholar 

  • Peters TM, Clark JA, Pike GB, Henri C, Collins L, Leksell D and Jeppsson O (1989). Stereotactic neurosurgery planning on a personal-computer-based work station. Journal of Digital Imaging 2:75–81.

    Google Scholar 

  • Baker HH and Binford TO (1981). Depth from edge and intensity based stereo. In: Proc. Seventh Intl. Joint Conf. on Artificial Intelligence. William Kaufmann Inc., Los Altos CA, pp. 631–636.

    Google Scholar 

  • Barnard ST and Thompson WB (1980). Disparity analysis of images. IEEE Trans. Pattern Anal. Machine Intell. PAMI-2:333–340.

    Google Scholar 

  • Barnard ST and Fischler MA (1987). Stereo vision. In: Encyclopedia of Artificial Intelligence. Shapiro SC (ed), Wiley, New York, pp. 1083–1090.

    Google Scholar 

  • Collorec R and Coatrieux JL (1988). Vectorial tracking and directed contour finder for vascular network in digital subtraction angiography. Pattern Recognition Letters 8:353–358

    Google Scholar 

  • Duda RO and Hart PE (1973). Pattern Recognition And Scene Analysis. Wiley, New York.

    Google Scholar 

  • Eichel PH, Delp EJ, Koral K and Buda AJ (1988). A method for a fully automatic definition of coronary arterial edges from cineangiograms. IEEE Transactions on Medical Imaging 7:313–320.

    Google Scholar 

  • Fencil LE and Metz CE (1990). Propagation and reduction of error in three dimensional structure determined from biplane views of unknown orientation. Medical Physics 17:951–961.

    Google Scholar 

  • Hoffmann KR, Doi K, Chan HP and Chua KG (1987). Computer reproduction of the vasculature using an automated tracking method. Proceedings of SPIE Medical Imaging 767:449–453.

    Google Scholar 

  • Horaud R and Skordas T (1989). Stereo correspondence through feature grouping and maximal cliques. IEEE Trans. Pattern Anal. Machine Intell. PAMI-11:1168–1180.

    Google Scholar 

  • Levine MD, O'Handley DA and Yagi GA (1973). Computer determination of depth maps. Computer Graphics and Image Processing 2:131–150.

    Google Scholar 

  • Markowsky G and Wesley MA (1980). Fleshing out wire frames. IBM J. Res. Develop. 24:582–597.

    Google Scholar 

  • Mawko GM (1989). Three-dimensional analysis of digital subtraction angiograms for stereotactic neurosurgery planning. PhD Thesis, McGill University.

    Google Scholar 

  • Metz CE and Fencil LE (1989). Determination of three-dimensional structure in biplane radiography without prior knowledge of the relationship between the two views:theory. Medical Physics 16:45–51.

    Google Scholar 

  • Mohan R, Medioni G and Nevatia R (1989). Stereo error detection, correction, and evaluation. IEEE Trans. Pattern Anal. Machine Intell. PAMI-11:113–120.

    Google Scholar 

  • Nguyen TV and Sklansky J (1986). Computing the skeleton of coronary arteries in cineangiograms. Computers and Biomedical Research 19:428–444.

    Google Scholar 

  • Prazdny K (1983). Stereoscopic matching, eye position, and absolute depth. Perception 12:151–160.

    Google Scholar 

  • Shamos MI (1978). Computational Geometry. PhD Thesis, Yale University.

    Google Scholar 

  • Smets C, Verbeeck G, Suetens P and Oosterlinck AJ (1988). A knowledge-based system for the delineation of blood vessels on subtraction angiograms. Pattern Recognition Letters 8:113–121.

    Google Scholar 

  • Smets C, Suetens P, Oosterlinck A and Van der Werf F (1989). A knowledge-based system for the labeling of the coronary arteries. In: Computer Assisted Radiology, Proc. 3rd Intl. Symp. CAR. Lemke HU (ed), Springer-Verlag, Berlin, pp. 322–326.

    Google Scholar 

  • Smets C (1990). A knowledge-based system for the automatic interpretation of blood vessels on angiograms. PhD Thesis, Leuven University.

    Google Scholar 

  • Sobel I (1974). On calibrating computer controlled cameras for perceiving 3-d scenes. Artificial Intelligence 5:185–198.

    Google Scholar 

  • Suetens P, Van Cleynenbreugel J, Fierens F, Smets C and Oosterlinck A (1987). An expert system for blood vessel segmentation on subtraction angiograms. Proceedings of SPIE Medical Imaging 767:454–459.

    Google Scholar 

  • Sun Y (1989). Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Transactions on Medical Imaging 8:78–88.

    Google Scholar 

  • Sutherland IE (1974). Three-dimensional data input by tablet. Proceedings of the IEEE 62:453–471.

    Google Scholar 

  • Wesley MA and Markowsky G (1981). Fleshing out projections. IBM J. Res. Develop. 25:934–954.

    Google Scholar 

Download references

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Alan C. F. Colchester David J. Hawkes

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© 1991 Springer-Verlag Berlin Heidelberg

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Henri, C.J., Collins, D.L., Peters, T.M. (1991). Reconstruction of 3-D branching structures. In: Colchester, A.C.F., Hawkes, D.J. (eds) Information Processing in Medical Imaging. IPMI 1991. Lecture Notes in Computer Science, vol 511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033743

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  • DOI: https://doi.org/10.1007/BFb0033743

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54246-9

  • Online ISBN: 978-3-540-47521-7

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