Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Gradient and Del Operator

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...

Three-Dimensional Analysis of Strain

Three-Dimensional Analysis of Strain

Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...

You might also read

Related Articles

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

Sort by

Same author

PLUTO: A Public Value Assessment Tool.

IEEE computer graphics and applications·2026

Same author

A Multidimensional Assessment Method for Visualization Understanding (MdamV).

IEEE transactions on visualization and computer graphics·2026

Same author

Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization.

IEEE transactions on visualization and computer graphics·2025

Same author

The Importance of Being Thorough: How Data Analysis Choices Impact the Perceived Relationship between Pollutants and Predictors.

Water research·2025

Same author

Visual Data Analysis of Time-Based Transport Optimizations.

IEEE computer graphics and applications·2025

Same author

Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data.

IEEE transactions on visualization and computer graphics·2024

Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026

Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026

Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026

Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026

Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026

Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Edge aware anisotropic diffusion for 3D scalar data.

Zahid Hosssain¹, Torsten Möller

¹School of Computer Science, Simon Fraser University, Burnaby, BC, Canada. zha13@cs.sfu.ca

IEEE Transactions on Visualization and Computer Graphics

|October 27, 2010

Summary

This summary is machine-generated.

This study introduces a new anisotropic diffusion model for 3D data. It effectively smooths noise while preserving crucial material boundaries and tubular structures, improving image analysis.

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Related Experiment Videos

Last Updated: Jun 7, 2026

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Computer Vision
Image Processing
Scientific Visualization

Background:

Anisotropic diffusion is crucial for noise reduction in 3D scalar fields.
Existing methods often struggle with preserving fine structures and boundary details.
Gradient magnitude thresholding can be sensitive to diffusion parameters.

Purpose of the Study:

To present a novel anisotropic diffusion model for 3D scalar field data.
To enhance noise smoothing while preserving material boundaries and tubular structures.
To reduce sensitivity to diffusion parameters compared to traditional methods.

Main Methods:

Developed a novel anisotropic diffusion model using the directional second derivative.
Utilized the directional second derivative for material boundary definition instead of gradient magnitude.
Analyzed the stability and convergence of the proposed diffusion model empirically.

Main Results:

The model effectively preserves material boundaries and fine tubular structures.
Noise is significantly smoothed out in the 3D scalar field data.
The diffusion model exhibits lower sensitivity to the diffusion parameter.
Consistent smoothing of material boundaries is achieved compared to gradient magnitude techniques.

Conclusions:

The proposed anisotropic diffusion model offers superior performance in noise reduction and structure preservation for 3D scalar fields.
The use of the directional second derivative provides a more robust and consistent boundary definition.
The model shows promise for applications in scientific visualization, particularly volume rendering.