CASS: A computer aided surgical system based on 3D medical images

The paper introduces a computer aided surgical system (CASS) based on the 3D medical images, which is designed for the surgeons to examine the tissues, acquire anatomical structures, diagnose illness and plan surgery. The system takes the 3D medical images as input and designs a segmentation pipeline to segment out the volumes of interested tissues. Then triangle meshes of the interested tissues were constructed by the discrete March Cubes algorithms, which is used to provide surface models for real time processing and rendering. To assist the surgeons with biomechanical simulation, several volume mesh generation approaches are provided to construct tetrahedral and hexahedral meshes for FEM. With this integrated models, the system can provide intuitive visualization of the slice images and focused medical models, as well as several effective computer aided diagnosis tools which are proved valuable in clinical practice.

[1]  Elaine Cohen,et al.  Volumetric parameterization and trivariate B-spline fitting using harmonic functions , 2009, Comput. Aided Geom. Des..

[2]  Bernhard Preim,et al.  Combining Silhouettes, Surface, and Volume Rendering for Surgery Education and Planning , 2005, EuroVis.

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

[4]  M. Yvinec,et al.  Variational tetrahedral meshing , 2005, SIGGRAPH 2005.

[5]  Steven E. Benzley,et al.  A Comparison of All Hexagonal and All Tetrahedral Finite Element Meshes for Elastic and Elasto-plastic Analysis , 2011 .

[6]  William E. Lorensen,et al.  Surface Rendering Versus Volume Rendering In Medical Imaging: Techniques And Applications , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[7]  Chi-Wing Fu,et al.  Parameterization of Star-Shaped Volumes Using Green's Functions , 2010, GMP.

[8]  Ying He,et al.  Direct-Product Volumetric Parameterization of Handlebodies via Harmonic Fields , 2010, 2010 Shape Modeling International Conference.

[9]  Joe Michael Kniss,et al.  Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets , 2001, Proceedings Visualization, 2001. VIS '01..

[10]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[11]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[12]  Nelson L. Max,et al.  Optical Models for Direct Volume Rendering , 1995, IEEE Trans. Vis. Comput. Graph..

[13]  Ralph Müller,et al.  Quantitative micro-computed tomography: a non-invasive method to assess equivalent bone mineral density. , 2008, Bone.

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

[15]  Desheng Wang,et al.  Tetrahedral mesh generation and optimization based on centroidal Voronoi tessellations , 2003 .

[16]  Andrew H. Gee,et al.  High resolution cortical bone thickness measurement from clinical CT data , 2010, Medical Image Anal..

[17]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[18]  Jian Liu,et al.  Novel 3D Reconstruction Modeling Contributes to Development of Orthopaedic Surgical Interventions , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[19]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[20]  Jason F. Shepherd,et al.  Hexahedral mesh generation constraints , 2008, Engineering with Computers.