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 Concept Videos

Vector Forms of Green’s Theorem01:26

Vector Forms of Green’s Theorem

The study of fluid motion often involves understanding how local rotational behavior relates to global circulation. In the context of a pond with pollutants, direct measurement of water movement along an irregular shoreline can be impractical. Green’s Theorem in vector form provides an alternative by relating the circulation around a closed boundary to properties of the flow within the enclosed region.Measurements of water velocity at different points define a continuous vector field that...
Significance of the Gradient Vector01:27

Significance of the Gradient Vector

A surface defined by a function of two variables can be understood by examining how it changes along specific directions. When one variable is held constant, the surface reduces to a curve that reflects variation in the other variable. For example, fixing one variable and moving parallel to a coordinate axis produces a cross-sectional curve. The slope of this curve at a given point represents how the function changes in that particular direction, providing a measure of local steepness.By...
Gradient Vectors and Their Applications01:19

Gradient Vectors and Their Applications

Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...

You might also read

Related Articles

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

Sort by
Same author

Understanding the "how" and "why": A mixed methods process evaluation for the PRO-HIIT intervention.

PloS one·2026
Same author

Interlimb differences in knee joint loading and stress distribution following anterior cruciate ligament reconstruction during stair descent.

Clinical biomechanics (Bristol, Avon)·2026
Same author

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Wavelet spectral-aware Kolmogorov-Arnold Network for organ and tumor segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Association of renal artery variations with aggressive pathological features and poor prognosis in clear-cell renal cell carcinoma: a computed tomography-based three-dimensional reconstruction study.

Quantitative imaging in medicine and surgery·2026
Same author

Causal effects of endometriosis on serum 25-hydroxyvitamin D: Evidence from Mendelian randomization study.

Medicine·2026
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

HiCAF-Net: A Hierarchical Cross-Attention Fusion framework for cross-cancer subtype classification using histopathological and genomic data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Gradient vector flow with mean shift for skin lesion segmentation.

Huiyu Zhou1, Gerald Schaefer, M Emre Celebi

  • 1Queen's University Belfast, Belfast, BT3 9DT, United Kingdom. H.Zhou@ecit.qub.ac.uk

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|September 14, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mean shift-based gradient vector flow (GVF) algorithm for improved skin lesion segmentation in dermoscopy images. The new method enhances accuracy by addressing common under- or over-segmentation issues found in traditional GVF techniques.

More Related Videos

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Related Experiment Videos

Last Updated: Jun 8, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

  • Dermatology
  • Medical Image Analysis
  • Computer Vision

Background:

  • Accurate skin lesion border extraction is crucial for diagnosing skin conditions from dermoscopy images.
  • Gradient Vector Flow (GVF) algorithms are widely used for image segmentation but often suffer from under- or over-segmentation due to energy force compromises.
  • Existing GVF methods struggle with precise boundary delineation in complex dermoscopy images.

Purpose of the Study:

  • To introduce a novel mean shift-based Gradient Vector Flow (GVF) algorithm for enhanced skin lesion segmentation.
  • To improve the accuracy of dermoscopy image analysis by addressing limitations of traditional GVF methods.
  • To develop a segmentation technique that accurately identifies skin lesion borders, aiding in diagnosis.

Main Methods:

  • Incorporation of a mean shift operation into the standard GVF cost function.
  • Development of a modified GVF algorithm to better direct internal and external energy forces.
  • Theoretical analysis to prove rapid convergence of the proposed algorithm.

Main Results:

  • The proposed mean shift-based GVF algorithm demonstrates accurate determination of skin lesion borders.
  • Experimental results on a diverse set of dermoscopy images validate the method's effectiveness.
  • The new approach successfully mitigates under- and over-segmentation problems inherent in standard GVF.

Conclusions:

  • The novel mean shift-based GVF algorithm offers a significant improvement for skin lesion segmentation in dermoscopy images.
  • This method provides a more accurate and reliable tool for dermatological image analysis.
  • The algorithm's ability to precisely delineate lesion borders can enhance diagnostic accuracy.