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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 GroupDepartment of Radiology - University of Pennsylvania

Philadelphia, PA

*Department of Mathematics and Computer ScienceSaint Joseph’s University

Philadelphia, PA

http://www.mipg.upenn.edu/~cavass

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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.

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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

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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.

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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.

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3D CAVA Software Systems (MIPG)

DISPLAY mini computer + frame buffer 1980

DISPLAY82 mini computer + frame buffer 1982 3D83 GE CT/T 8800 1983 3D98 GE CT/T 9800 1986 3DPC PC-based 1989 3DVIEWNIX Unix, X-Windows 1993 CAVASS platform independent, wxWidgets 2007

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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.

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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

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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

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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

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Results

Regular: 25625646, MR brain image (6 MB)

Large: 512512459, CT of thorax (241 MB)

Super: 10231023417, 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.

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Results

Operation SystemRegular Large Super

seq parallel seq parallel seq parallel

InterpolationITK 2.9 1.7 [2] 87.7 62.8 [2] Failed Failed [2]

CAVASS 0.6 1 [2] 54.9 14.9 [2] 139.1 49.2 [2]

AnisotropicDiffusive Filtering

ITK 57 2206.6

CAVASS 52.7 1664.2

Gaussian

Filtering

ITK 1.5 65.2 Failed

CAVASS 0.4 18.3 83

Distance

Transform

ITK 10.5 473.7 Failed

CAVASS 18.7 916.5 3382.4

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Results

Operation SystemRegular Large Super

seq parallel seq parallel seq parallel

ThresholdingITK 0.3 11.4 340.6

CAVASS 0.1 2.7 20.2

Fuzzy ConnectedSegmentation

ITK 108.4 Failed Failed

CAVASS 49.5 17.8 [5] 843.7 298.6 [5] Failed 1312.6 [5]

Registration

(rigid)

ITK 57.2 Failed Failed

CAVASS 56.1 8.6 [5] 1860.6 301.6 [5] 3863.4 1089.1 [5]

Registration (affine - 12 parameters)

ITK 208.3 Failed Failed

CAVASS 155.3 25.1 [5] 3602.4 1018.6 [5]

13,111 3662.2 [5]

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Results

Surface Rendering:

Data Set CAVASS seq

/no aaCAVASS seq/aa

VTK

Regular 0.03 0.06 0.29

Large 0.11 0.19 0.41

Super 0.16 0.26 1.38

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Results

Volume Rendering:

Data Set CAVASS VTK

sequential parallel Ray Casting 2D Texture

Regular 0.56 0.06 [6] 1.09 1.20

Large 3.53 1.36 [6] 5.03 18.32

Super 9.77 3.66 [6] 6.94 > 240.00

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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.

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Further Information

www.mipg.upenn.edu/~cavass

Release date: July/August 2007

Other papers: 6509-03 – Visualization

6509-66 – Visualization

6519-07 – PACS

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Issues

(1) How to evaluate open source systems.

(2) Quality assurance in open source software (correctness, accuracy, efficiency,….).