An Introduction and Demonstration of a New Computer Assisted Visualization and Analysis
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An Introduction and Demonstration of a New Computer Assisted Visualization and Analysis Software System ( CAVASS ). Jayaram K. Udupa + , George J. Grevera *+ , Dewey Odhner + , Ying Zhuge + , Andre Souza + , Tad Iwanaga + , Shipra Mishra +. + Medical Image Processing Group

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Jayaram k udupa george j grevera dewey odhner
An Introduction and Demonstration of a New Computer Assisted Visualization and Analysis Software System (CAVASS)

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

Introduction Visualization and Analysis


To present a new cluster-based open-source software system called CAVASS (next incarnation of 3DVIEWNIX)for image analysis and visualization.

Goal of CAVASS:To achieve practical processing time on even

very large data sets.

CAVA: Computer-Aided Visualization and Analysis

CAVASS: CAVA Software System

CAVA deals withthe science underlying computerized methods of image processing, analysis, and visualization to facilitate new therapeutic strategies, basic clinical research, education, and training.

Cava operations in cavass
CAVA Visualization and Analysis Operations in CAVASS

Image processing: for enhancing information about and defining object system in images.

Visualization:for viewing and comprehending object system in its full form, shape, and dynamics.

Manipulation:for altering object system (virtual surgery).

Analysis:for quantifying information about object system.

CAVA operations take object system information from one space to another typically, and eventually also to a quantitative space.

Previous software systems brought out by our group
Previous Visualization and Analysis software systems brought out by our group:

DISPLAY mini computer + frame buffer 1980

DISPLAY82 mini computer + frame buffer 1982

(distributed to > 150 sites with source.)

3D83 GE CT/T 8800 1983

3D98 GE CT/T 9800 1986

3DPCPC-based 1989

3DVIEWNIX Unix, X-Windows 1993

(distributed with source to 100s of sites.)

CAVASS platform independent, wx Widget 2008

Jayaram k udupa george j grevera dewey odhner

CAVASS Visualization and Analysis Target User Groups

UG1 – CAVA basic researchers/technology developers

UG2 – CAVA application developers

UG3 – Users of CAVA methods in clinical research.

Current Open Source Software Limitations that CAVASS Attempts to Address

LM1 – Lack of comprehensiveness of CAVA operations.

LM2 – Lack of coverage for user groups UG1-UG3.

LM3 – Lack of adequate speed/efficiency.

LM4 – Lack of adequate interfaces.

Jayaram k udupa george j grevera dewey odhner

Methods Visualization and Analysis

Key Features of CAVASS

(F1) Open-source, C/C++, wxWidgets.

(F2) Inherits most CAVA functions of 3DVIEWNIX.

(F3) Incorporates most commonly used CAVA operations, but does not go overboard on generality and inclusiveness.

(F4) Optimized implementations for efficiency.

(F5) Time intensive operations parallelized and implemented using Open MPI on a cluster of workstations (COWs).

(F6) Interfaces to popular toolkits (ITK, VTK), CAD/CAM formats, DICOM support, other popular formats.

(F7) Stereo interface for visualization.

Parallelization of cava operations in cavass
Parallelization of Visualization and Analysis CAVA Operations in CAVASS

Divide the input image into chunks and assign each chunk to a processor. A chunk represents data contained in a contiguous set of slices, either image or object structure data.

CAVA operations can be divided into the following three groups.

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.

CAVASS parallelizes all three groups of operations.

Results Visualization and Analysis

Test Data Sets

Sequential and parallel implementations of several Type 1 and Type 3 operations in CAVASS and ITK/VTK are compared using three data sets:

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)


Multiprocessor system: 3.4 GHz, dual processor, 4GB RAM.

COW: 3.4 GHz single processor systems, 1GB/sec connection.

Jayaram k udupa george j grevera dewey odhner

In the following tables, the number of processors used is shown in square brackets under “parallel”. The times reported are in seconds. No entries indicate that the operation was either not tested or not available.

Jayaram k udupa george j grevera dewey odhner

* shown in VTK rendering is assisted by a graphics processor. CAVASS

rendering is entirely in software.

Jayaram k udupa george j grevera dewey odhner

Conclusions shown in

  • COWs are more cost/speed effective than multi-processing systems. They are seemlessly expandable and upgradeable without requiring software changes.

  • Most CAVA operations can be accomplished in reasonable time even for extremely large data sets on COWs in portable software.

  • (3) COWs can be built quite inexpensively in CAVA research labs with publicly available hardware and software and standards.

  • (4) CAVASS can handle extremely large data sets. It seems to be considerably faster than ITK in many image processing operations.


CAVASS release date: April/May 2008.