3D Bone Shape Modelling Basing on Dataset Recorded by Ultrasound Free-Hand Navigated Probe

The aim of this paper was to analyze the spatial geometry of objects basing on ultrasound images recorded by free-hand probe. The data analysis consists of three phases: data recording, contour segmentation and triangulation. Several algorithms of segmentation have been tested to recognize the object’s contour. To analyze the efficiency of triangulation algorithms, the similarities of obtained 3D models to the reference CT-based 3D model were evaluated. For objective and quantitative evaluation of obtained models the Hausdorff’s distances were calculated for compared surfaces. The results revealed higher efficiency of greedy projection method than the 2D Delaunay’s Triangulation applied for projected set of points in step by step procedure for the same cloud of points obtained as the result of user controlled segmentation procedure. The free hand ultrasonography can be applied to measure the spatial tissue shape for virtual planning of surgery without radiation.

[1]  William J. Schroeder,et al.  Overview of Visualization , 2005, The Visualization Handbook.

[2]  K Krysztoforski,et al.  New approach to bone surface reconstruction from 2: 5D sonographic dataset , 2011 .

[3]  Zoltan-Csaba Marton,et al.  On fast surface reconstruction methods for large and noisy point clouds , 2009, 2009 IEEE International Conference on Robotics and Automation.

[4]  Jérôme Tonetti,et al.  An Information Fusion Method for the Automatic Delineation of the Bone-Soft Tissues Interface in Ultrasound Images , 2004, ECCV Workshops CVAMIA and MMBIA.

[5]  K Krysztoforski,et al.  Noninvasive ultrasonic measuring system for bone geometry examination , 2011, The international journal of medical robotics + computer assisted surgery : MRCAS.

[6]  Dinesh K. Pai,et al.  Direct surface extraction from 3D freehand ultrasound images , 2002, IEEE Visualization, 2002. VIS 2002..

[7]  Pierrick Coupé,et al.  3D Freehand Ultrasound Reconstruction Based on Probe Trajectory , 2005, MICCAI.

[8]  Maike Buchin,et al.  Can We Compute the Similarity between Surfaces? , 2007, Discret. Comput. Geom..

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

[10]  Zoltan-Csaba Marton,et al.  On Fast Surface Reconstruction Methods for Large and Noisy Datasets , 2009, IEEE International Conference on Robotics and Automation.

[11]  Ewelina Świątek-Najwer,et al.  The investigation of the lower limb geometry using 3D sonography and magnetic resonance , 2012 .

[12]  Ioannis A. Kakadiaris,et al.  Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis , 2004, Lecture Notes in Computer Science.

[13]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[14]  Guido Gerig,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II , 2005, MICCAI.

[15]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.