CAVASS: a computer assisted visualization and analysis software system - visualization aspects

The Medical Image Processing Group (MIPG) at the University of Pennsylvania has been developing and distributing medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available, open source, and is integrated with toolkits such as ITK and VTK. CAVASS runs on Windows, Unix, and Linux but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive COWs (Cluster of Workstations) for more time consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of medical imagery, so support for 3D and higher dimensional medical image data and the efficient implementation of algorithms is given paramount importance. This paper focuses on aspects of visualization. All of the most of the popular modes of visualization including various 2D slice modes, reslicing, MIP, surface rendering, volume rendering, and animation are incorporated into CAVASS.

[1]  W. Bilker,et al.  Late-onset minor and major depression: early evidence for common neuroanatomical substrates detected by using MRI. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  J K Udupa,et al.  Correlation of volumetric magnetization transfer imaging with clinical data in MS , 1998, Neurology.

[3]  Jayaram K. Udupa,et al.  Near-automatic quantification of breast tissue glandularity via digitized mammograms , 1999, Medical Imaging.

[4]  L G Nyúl,et al.  Numerical tissue characterization in MS via standardization of the MR image intensity scale , 2000, Journal of magnetic resonance imaging : JMRI.

[5]  J. Udupa,et al.  The geometric architecture of the subtalar and midtarsal joints in rheumatoid arthritis based on magnetic resonance imaging. , 2002, Arthritis and rheumatism.

[6]  Jayaram K. Udupa,et al.  Comparison of T 2 Lesion Volume and Magnetization Transfer Ratio Histogram Analysis and of Atrophy and Measures of Lesion Burden in Patients with Multiple Sclerosis , 1998 .

[7]  W. Bilker,et al.  Atrophy and High Intensity Lesions , 2000, Neuropsychopharmacology.

[8]  J K Udupa,et al.  Comparison of T2 lesion volume and magnetization transfer ratio histogram analysis and of atrophy and measures of lesion burden in patients with multiple sclerosis. , 1998, AJNR. American journal of neuroradiology.

[9]  Jayaram K. Udupa,et al.  Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds , 2001, Comput. Vis. Image Underst..

[10]  Stanley Osher,et al.  Level Set Methods , 2003 .

[11]  Jayaram K. Udupa,et al.  Volume rendering in the presence of partial volume effects , 2005, IEEE Transactions on Medical Imaging.

[12]  Jayaram K. Udupa,et al.  A characterization of the geometric architecture of the peritalar joint complex via MRI, an aid to classification of foot type , 1999, IEEE Transactions on Medical Imaging.

[13]  E Stindel,et al.  3D MR image analysis of the morphology of the rear foot: application to classification of bones. , 1999, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[14]  Jayaram K. Udupa,et al.  Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  J. Udupa,et al.  A new computer-assisted method for the quantification of enhancing lesions in multiple sclerosis. , 1997, Journal of computer assisted tomography.

[16]  Jayaram K. Udupa,et al.  Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation , 2001, Comput. Vis. Image Underst..

[17]  Jayaram K. Udupa,et al.  Scale-based method for correcting background intensity variation in acquired images , 2002, SPIE Medical Imaging.

[18]  Gabor T. Herman,et al.  3D83 - An Easy-To-Use Software Package For Three-Dimensional Display From Computed Tomograms , 1984, Other Conferences.

[19]  Mark C Preul,et al.  Interactive stereoscopic virtual reality: a new tool for neurosurgical education. Technical note. , 2002, Journal of neurosurgery.

[20]  William E. Higgins,et al.  Shape-based interpolation of tree-like structures in three-dimensional images , 1993, IEEE Trans. Medical Imaging.

[21]  William E. Lorensen,et al.  Marching cubes: a high resolution 3D surface construction algorithm , 1996 .

[22]  Jayaram K. Udupa,et al.  Interactive segmentation and boundary surface formation for 3-D digital images , 1982, Comput. Graph. Image Process..

[23]  Jacques-Olivier Lachaud,et al.  Continuous Analogs of Digital Boundaries: A Topological Approach to Iso-Surfaces , 2000, Graph. Model..

[24]  Jayaram K. Udupa,et al.  Shell manipulation: interactive alteration of multiple-material fuzzy structures , 1995, Medical Imaging.

[25]  J. Udupa,et al.  Shape-based interpolation of multidimensional objects. , 1990, IEEE transactions on medical imaging.

[26]  Dewey Odhner,et al.  3DVIEWNIX: an open, transportable, multidimensional, multimodality, multiparametric imaging software system , 1994, Medical Imaging.

[27]  R A Robb,et al.  Analyze: a comprehensive, operator-interactive software package for multidimensional medical image display and analysis. , 1989, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[28]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Jayaram K. Udupa,et al.  Artery-vein separation via MRA-An image processing approach , 2001, IEEE Transactions on Medical Imaging.

[30]  Jayaram K. Udupa,et al.  Fast visualization, manipulation, and analysis of binary volumetric objects , 1991, IEEE Computer Graphics and Applications.

[31]  Ponnada A. Narayana,et al.  Detection and delineation of multiple sclerosis lesions in gadolinium-enhanced 3D T1-weighted MRI data , 2000, Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000.

[32]  A. Pack,et al.  Upper airway size analysis by magnetic resonance imaging of children with obstructive sleep apnea syndrome. , 2003, American journal of respiratory and critical care medicine.

[33]  Y. Censor,et al.  Demonstration of a software package for the reconstruction of the dynamically changing structure of the human heart from cone beam X-ray projections , 2005, Journal of Medical Systems.

[34]  Jayaram K. Udupa,et al.  New automatic mode of visualizing the colon via Cine CT , 2001, SPIE Medical Imaging.

[35]  Carolyn A. Bucholtz,et al.  Shape-based interpolation , 1992, IEEE Computer Graphics and Applications.

[36]  J K Udupa,et al.  Computer-assisted quantitation of enhancing lesions in multiple sclerosis: correlation with clinical classification. , 1997, AJNR. American journal of neuroradiology.

[37]  Jayaram K. Udupa.  Multidimensional Digital Boundaries , 1994, CVGIP Graph. Model. Image Process..

[38]  Taran Rampersad.  wxWindows for cross-platform coding , 2003 .

[39]  Jayaram K. Udupa,et al.  Clutter‐free volume rendering for magnetic resonance angiography using fuzzy connectedness , 2000 .

[40]  J. B. Kneeland,et al.  Analysis of in vivo 3-D internal kinematics of the joints of the foot [MRI analysis] , 1998, IEEE Transactions on Biomedical Engineering.

[41]  Jayaram K. Udupa,et al.  Comparative analysis of shell rendering and shear-warp rendering , 2002, SPIE Medical Imaging.

[42]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  J. K. Udupa,et al.  Isolated U-fiber involvement in MS , 1998, Neurology.

[44]  Jayaram K. Udupa,et al.  Multidimensional data format specification: A generalization of the American College of Radiology-National Electric Manufacturers Association standards , 1992, Journal of Digital Imaging.

[45]  Jayaram K. Udupa,et al.  Brain tumor segmentation in MRI by using the fuzzy connectedness method , 2001, SPIE Medical Imaging.

[46]  Jayaram K. Udupa,et al.  Shape-based interpolation of multidimensional grey-level images , 1994, Medical Imaging.

[47]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[48]  J K Udupa,et al.  Brain atrophy in relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis: longitudinal quantitative analysis. , 2000, Radiology.

[49]  J K Udupa,et al.  Global volumetric estimation of disease burden in multiple sclerosis based on magnetization transfer imaging. , 1997, AJNR. American journal of neuroradiology.

[50]  Sargur N. Srihari,et al.  Boundary Detection in Multidimensions , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Vadim Zeitlin.  The wxWindows cross-platform framework , 2001 .

[52]  Andrew F. Laine,et al.  Adaptive speed term based on homogeneity for level-set segmentation , 2002, SPIE Medical Imaging.

[53]  J. Udupa,et al.  Tarsal joint kinematics via 3D imaging. , 2000, Critical reviews in diagnostic imaging.

[54]  Jayaram K. Udupa,et al.  An Order of Magnitude Faster Isosurface Rendering in Software on a PC than Using Dedicated, General Purpose Rendering Hardware , 2000, IEEE Trans. Vis. Comput. Graph..

[55]  László G. Nyúl,et al.  Virtual dissection of the colon: technique and first experiments with artificial and cadaveric phantoms , 2002, SPIE Medical Imaging.

[56]  Max A. Viergever,et al.  Objective and reproducible segmentation and quantification of tuberous sclerosis lesions in FLAIR brain MR images , 2001, SPIE Medical Imaging.

[57]  J K Udupa,et al.  Relapsing-remitting multiple sclerosis: longitudinal analysis of MR images--lack of correlation between changes in T2 lesion volume and clinical findings. , 1999, Radiology.

[58]  Dan Gordon,et al.  Back-to-Front Display of Voxel Based Objects , 1985, IEEE Computer Graphics and Applications.

[59]  Jayaram K. Udupa,et al.  Upper airway segmentation and measurement in MRI using fuzzy connectedness , 2002, SPIE Medical Imaging.

[60]  J. Udupa,et al.  Three-dimensional Bone-free Rendering of the Cerebral Circulation by Use of Computed Tomographic Angiography and Fuzzy Connectedness , 2002, Neurosurgery.

[61]  Jayaram K. Udupa,et al.  T-shell rendering , 2001, SPIE Medical Imaging.

[62]  Noël Bonnet,et al.  Two Methods for Semi-automatic Image Segmentation based on Fuzzy Connectedness and Watersheds , 2001, VIIP.

[63]  W A Barrett,et al.  A PC-based 3D imaging system: algorithms, software, and hardware considerations. , 1990, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[64]  J.K. Udupa,et al.  Display of 3D information in discrete 3D scenes produced by computerized tomography , 1983, Proceedings of the IEEE.

[65]  Gabor T. Herman,et al.  3D98: A Turnkey System For The 3D Display And Analysis Of Medical Objects In CT Data , 1986, Other Conferences.

[66]  Gabor Herman,et al.  Display of 3-D Digital Images: Computational Foundations and Medical Applications , 1983, IEEE Computer Graphics and Applications.

[67]  Jayaram K. Udupa,et al.  Scale-based diffusive image filtering preserving boundary sharpness and fine structures , 2001, IEEE Transactions on Medical Imaging.

[68]  Jayaram K. Udupa,et al.  Automatic bone-free rendering of cerebral aneurysms via 3D CTA , 2001, SPIE Medical Imaging.

[69]  Jayaram Udupa,et al.  Age of onset of depression and quantitative neuroanatomic measures: absence of specific correlates , 1999, Psychiatry Research: Neuroimaging.

[70]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[71]  J.K. Udupa,et al.  Kinematics of joints of the foot via three-dimensional; magnetic resonance images , 1990, [1990] Proceedings of the First Conference on Visualization in Biomedical Computing.

[72]  M. Horsfield,et al.  Quantitative assessment of magnetic resonance imaging lesion load in multiple sclerosis. , 1998, Journal of neurology, neurosurgery, and psychiatry.

[73]  Supun Samarasekera,et al.  Multiple sclerosis lesion quantification using fuzzy-connectedness principles , 1997, IEEE Transactions on Medical Imaging.

[74]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[75]  Dimitris N. Metaxas,et al.  Image-based ventricular blood flow analysis , 1998 .

[76]  Jayaram K. Udupa,et al.  Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation , 2000, Comput. Vis. Image Underst..

[77]  Jayaram K. Udupa,et al.  A new in vivo technique for three-dimensional shoulder kinematics analysis , 1998, Skeletal Radiology.

[78]  M. Levoy,et al.  Fast volume rendering using a shear-warp factorization of the viewing transformation , 1994, SIGGRAPH.

[79]  Forum Mpi.  MPI: A Message-Passing Interface , 1994 .

[80]  Guido Gerig,et al.  Nonlinear anisotropic filtering of MRI data , 1992, IEEE Trans. Medical Imaging.

[81]  J. Udupa,et al.  Estimation of tumor volume with fuzzy-connectedness segmentation of MR images. , 2002, AJNR. American journal of neuroradiology.

[82]  Jayaram K. Udupa,et al.  Shell rendering , 1993, IEEE Computer Graphics and Applications.