Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

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

George Grevera1, Jayaram Udupa, Dewey Odhner

  • 1Department of Mathematics and Computer Science, Saint Joseph's University, 5600 City Avenue, Philadelphia, PA 19131, USA. ggrevera@sju.edu

Journal of Digital Imaging
|September 6, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI.

Medical physics·2025
Same author

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

International journal of molecular sciences·2025
Same author

From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.

PLOS digital health·2025
Same author

Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study.

Diagnostics (Basel, Switzerland)·2025
Same author

LncRNA MIR181A1HG Deficiency Attenuates Vascular Inflammation and Atherosclerosis.

Circulation research·2025
Same author

Response Theory via Generative Score Modeling.

Physical review letters·2025
Same journal

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Journal of digital imaging·2023
Same journal

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Journal of digital imaging·2023
Same journal

DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.

Journal of digital imaging·2023
Same journal

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging·2023
Same journal

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

Journal of digital imaging·2023
Same journal

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Journal of digital imaging·2023
See all related articles

The new CAVASS software offers open-source, parallelized medical image analysis and visualization for multi-dimensional data. It enhances previous systems, running on multiple platforms with efficient algorithm implementation.

Area of Science:

  • Medical Image Analysis
  • Scientific Visualization
  • Open-Source Software Development

Background:

  • The University of Pennsylvania's Medical Image Processing Group has a history of developing medical image analysis and visualization software, including the 3DVIEWNIX system released in 1993.
  • Significant advancements in computing, networking, parallel processing, and open-source toolkits have occurred since the development of previous systems.
  • There is a need for advanced, accessible software for the complex analysis and visualization of multi-dimensional medical imagery.

Purpose of the Study:

  • To introduce CAVASS as the next-generation medical image analysis and visualization system.
  • To provide a freely available, open-source software solution that leverages modern computing advancements.
  • To focus on efficient processing and visualization of 3D and higher-dimensional medical data.

Related Experiment Videos

Main Methods:

  • CAVASS is developed as the successor to 3DVIEWNIX, integrating with toolkits like Insight Toolkit and Visualization Toolkit.
  • The software is designed with a single codebase, ensuring compatibility across Windows, Unix, Linux, and Mac operating systems.
  • It utilizes inexpensive workstation clusters for parallel processing of computationally intensive algorithms, avoiding the need for expensive multiprocessor systems.

Main Results:

  • CAVASS is freely available and open source, promoting accessibility in medical image analysis.
  • The system supports parallel processing through clusters, enhancing efficiency for complex algorithms.
  • Paramount importance is given to supporting Digital Imaging and Communications in Medicine (DICOM) data and efficient algorithm implementation for multi-dimensional medical imagery.

Conclusions:

  • CAVASS represents a significant advancement in medical image analysis and visualization software.
  • Its open-source nature, cross-platform compatibility, and parallel processing capabilities make it a powerful tool for researchers and clinicians.
  • The software is optimized for the demanding requirements of 3D and higher-dimensional medical image processing and analysis.