1. 1 CAVASS: Computer Assisted Visualization and Analysis Software System Jayaram K. Udupa, George J. Grevera * Dewey Odhner, Ying Zhuge, Andre Souza, Tad Iwanaga, Shipra Mishra Medical Image Processing Group Department of Radiology - University of Pennsylvania Philadelphia, PA * Department of Mathematics and Computer Science Saint Joseph’s University Philadelphia, PA http://www.mipg.upenn.edu/~cavass

2. 2 CAVA CAVA: Computer-Aided Visualization and Analysis The science underlying computerized methods of image processing, analysis, and visualization to facilitate new therapeutic strategies, basic clinical research, education, and training.

3. 3 CAD vs CAVA CAD: Computer-Aided Diagnosis The science underlying computerized methods of image processing, visualization, and analysis for the diagnosis of diseases via images

4. 4 Purpose of CAVA In:Multiple multimodality multidimensional images of an object system. Out:Qualitative/quantitative information about objects in the object system. Object system – a collection of rigid, deformable, static, or dynamic, physical or conceptual objects.

5. 5 CAVA Operations Image processing: for enhancing information about and defining object system. Visualization:for viewing and comprehending object system. Manipulation:for altering object system (virtual surgery). Analysis:for quantifying information about object system.

6. 6 3D CAVA Software Systems (MIPG) DISPLAYmini computer + frame buffer 1980 DISPLAY82mini computer + frame buffer1982 3D83GE CT/T 88001983 3D98GE CT/T 98001986 3DPCPC-based1989 3DVIEWNIXUnix, X-Windows1993 CAVASSplatform independent, wxWidgets2007

7. 7 CAVA User Groups UG1 – CAVA basic researchers/technology developers UG2 – CAVA application developers UG3 – Users of CAVA methods in clinical research UG4 – Clinical end users in patient care CAVASS is aimed at UG1-UG3.

8. 8 Key Features of CAVASS Open source, C/C ++, wxWidgets Inherits most CAVA functions of 3DVIEWNIX Incorporates most commonly used CAVA operations Optimized implementations for efficiency Time intensive operations parallelized and implemented using Open MPI on a cluster of workstations (COWs) Interfaces to popular toolkits (ITK, VTK), CAD/CAM formats, DICOM support, other popular formats Stereo interface for visualization

9. 9 CAVA Operations in CAVASS Image Processing: VOI, Filtering, Interpolation, Segmentation, Registration, Morphological, Algebraic Visualization: Slice, Montage, Reslice, Roam through, Color overlay, MIP, GMIP, Surface rendering, Volume rendering Manipulation: Cut, Separate, Move, Reflect, Reposition, hard and fuzzy objects Analysis: Intensity profile/statistics, Linear, Angular, Area,Volume, Architecture /shape of objects, Kinematics

10. 10 Parallelization of CAVA Operations Approach:Chunking Chunk – data contained in a contiguous set of slices Type-1:Operation chunk-by-chunk, each chunk accessed only once. Ex: slice interpolation Type-2:As in Type-1, but significant further operation needed to combine results. Ex: 3D rendering Type-3:Operation chunk-by-chunk, but each chunk may have to be accessed more than once. Ex: graph traversal

11. 11 Results Regular: 256  256  46, MR brain image (6 MB) Large: 512  512  459, CT of thorax (241 MB) Super: 1023  1023  417, CT of head (873 MB) (visible woman) Sequential and parallel implementations of several Type-1, Type-2, Type-3 operations in CAVASS, ITK, VTK compared.

12. 12 Results OperationSystem RegularLargeSuper seqparallelseqparallelseqparallel Interpolation ITK2.91.7 [2]87.762.8 [2]FailedFailed [2] CAVASS0.61 [2]54.914.9 [2]139.149.2 [2] Anisotropic Diffusive Filtering ITK572206.6 CAVASS52.71664.2 Gaussian Filtering ITK1.565.2Failed CAVASS0.418.383 Distance Transform ITK10.5473.7Failed CAVASS18.7916.53382.4

13. 13 Results OperationSystem RegularLargeSuper seqparallelseqparallelseqparallel Thresholding ITK0.311.4340.6 CAVASS0.12.720.2 Fuzzy Connected Segmentation ITK108.4Failed CAVASS49.517.8 [5]843.7298.6 [5]Failed1312.6 [5] Registration (rigid) ITK57.2Failed CAVASS56.18.6 [5]1860.6301.6 [5]3863.41089.1 [5] Registration (affine - 12 parameters) ITK208.3Failed CAVASS155.325.1 [5]3602.41018.6 [5] 13,1113662.2 [5]

14. 14 Results Surface Rendering: Data Set CAVASS seq /no aa CAVASS seq/aa VTK Regular 0.030.060.29 Large 0.110.190.41 Super 0.160.261.38

15. 15 Results Volume Rendering: Data Set CAVASSVTK sequentialparallelRay Casting2D Texture Regular0.560.06 [6]1.091.20 Large3.531.36 [6]5.0318.32 Super9.773.66 [6]6.94> 240.00

16. 16 Conclusions (1)COWs are more cost/speed effective than multi- processing systems and are expandable. (2)Most CAVA operations can be accomplished in reasonable time on COWs in portable software. (3)COWs can be built quite inexpensively with publicly available hardware / software and standards. (4)CAVASS can handle very large data sets; considerably faster than ITK.

17. 17 Further Information www.mipg.upenn.edu/~cavass Release date: July/August 2007 Other papers: 6509-03 – Visualization 6509-66 – Visualization 6519-07 – PACS

18. 18 Issues (1)How to evaluate open source systems. (2) Quality assurance in open source software (correctness, accuracy, efficiency,….).