Identification and reconstruction of bullets from multiple X-rays

We present a framework for the rapid detection and 3D localisation of bullets (or other compact shapes) from a sparse set of cross-sectional patient x-rays. The intention of this work is to assess a software architecture for an application specific alternative to conventional CT which can be leveraged in poor communities using less expensive technology. Of necessity such a system will not provide the diagnostic sophistication of full CT, but in many cases this added complexity may not be required. While a pair of x-rays can provide some 3D positional information to a clinician, such an assessment is qualitative and occluding tissue/bone may lead to an incorrect assessment of the internal location of the bullet.Our system uses a combination of model-based segmentation and CT-like back-projection to arrive at an approximate volume representation of the embedded shape, based on a sequence of x-rays which encompasses the affected area. Depending on the nature of the injury, such a 3D shape approximation may provide sufficient information for surgical intervention.The results of our proof-of-concept study show that, algorithmically, such system is indeed realisable: a 3D reconstruction is possible from a small set of x-rays, with only a small computational load. A combination of real x-rays and simulated 3D data are used to evaluate the technique.

[1]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[2]  Mattieu de Villiers.  Limited angle tomography , 2004 .

[3]  Timothy F. Cootes,et al.  Active Shape Models - 'smart snakes' , 1992, BMVC.

[4]  Kevin J. Dalton,et al.  3D shape reconstruction using volume intersection techniques , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Clair L. Stong.  The Scientific American Book of Projects for the Amateur Scientist , 1987 .

[6]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  Djemel Ziou,et al.  Edge Detection Techniques-An Overview , 1998 .

[8]  Ali Mohammad-Djafari,et al.  Multiresolution approach to the estimation of the shape of a 3D compact object from its radiographic data , 1999, Optics & Photonics.

[9]  Sylvain Petitjean,et al.  A Computational Geometric Approach to Visual Hulls , 1998, Int. J. Comput. Geom. Appl..

[10]  P. Undrill,et al.  The use of texture analysis to delineate suspicious masses in mammography. , 1995, Physics in medicine and biology.

[11]  L. Rodney Long,et al.  Segmentation and feature extraction of cervical spine x-ray images , 1999, Medical Imaging.

[12]  M S Brown,et al.  Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[13]  Olli Nevalainen,et al.  Segmenting Bones from Wristhand Radiographs , 2000 .

[14]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[15]  R. D. Merrill.  Representation of contours and regions for efficient computer search , 1973, CACM.

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nikitas A. Alexandridis,et al.  Picture decomposition, tree data-structures, and identifying directional symmetries as node combinations , 1978 .

[18]  John Porrill,et al.  Active region models for segmenting textures and colours , 1995, Image Vis. Comput..

[19]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Wojciech Matusik,et al.  Creating and Rendering Image-Based Visual Hulls , 1999 .

[21]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[22]  Michael Brady,et al.  A Representation for Mammographic Image Processing , 1995, CVRMed.

[23]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[24]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Timothy F. Cootes,et al.  Active shape models , 1998 .

[26]  R. Deriche,et al.  Geodesic Active Regions for Texture Segmentation , 1998 .

[27]  Jake K. Aggarwal,et al.  Volume/surface octrees for the representation of three-dimensional objects , 1986, Comput. Vis. Graph. Image Process..

[28]  Georg Thimm.  Segmentation of X-ray Image Sequences Showing the Vocal Tract (with tool documentation) , 1999 .

[29]  John Porrill,et al.  EVERYTHING YOU ALWAYS WANTED TO KNOW ABOUT SNAKES (BUT WERE AFRAID TO ASK) , 2000 .

[30]  Jake K. Aggarwal,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2008 .

[31]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[32]  Michael Potmesil.  Generating octree models of 3D objects from their silhouettes in a sequence of images , 1987, Comput. Vis. Graph. Image Process..

[33]  M. Glas,et al.  Principles of Computerized Tomographic Imaging , 2000 .

[34]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[35]  Charles R. Dyer,et al.  Experiments on Picture Representation Using Regular Decomposition , 1976 .

[36]  Jyrki Lötjönen,et al.  Reconstruction of 3-D geometry using 2-D profiles and a geometric prior model , 1999, IEEE Transactions on Medical Imaging.

[37]  Paolo Cignoni,et al.  Metro: Measuring Error on Simplified Surfaces , 1998, Comput. Graph. Forum.

[38]  William E. Lorensen,et al.  The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics , 1998 .

[39]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  山田 寛喜,et al.  反復法を用いた伝導体の Tomographic Imaging , 1997 .

[41]  A D Marshall,et al.  Geometric Modelling for Computer Vision , 1992 .

[42]  Richard Szeliski,et al.  Rapid octree construction from image sequences , 1993 .

[43]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[44]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[45]  John Porrill,et al.  Statistical Snakes: Active Region Models , 1994, BMVC.

[46]  Tai Sing Lee,et al.  Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model , 1992, ECCV.

[47]  Narendra Ahuja,et al.  EFFICIENT OCTREE GENERATION FROM SILHOUETTES. , 1986 .

[48]  A. Siebert.  Dynamic Region Growing , 1997 .

[49]  Thomas S. Huang,et al.  Image processing , 1971 .

[50]  Wojciech Matusik,et al.  Polyhedral Visual Hulls for Real-Time Rendering , 2001, Rendering Techniques.