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

The Representativeness Heuristic02:13

The Representativeness Heuristic

15.4K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.4K
Ratio Level of Measurement00:54

Ratio Level of Measurement

13.7K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
13.7K
Relative Frequency Histogram01:14

Relative Frequency Histogram

4.7K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
4.7K
Histogram01:05

Histogram

12.7K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
12.7K
Probability Histograms01:17

Probability Histograms

8.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
8.7K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

451
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
451

You might also read

Related Articles

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

Sort by
Same author

Diffusing caveolin-1 scaffolds regulate mechanosignalling.

Nature cell biology·2026
Same author

HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy.

bioRxiv : the preprint server for biology·2025
Same author

nERdy: network analysis of endoplasmic reticulum dynamics.

Communications biology·2025
Same author

Physician-in-the-Loop Active Learning in Radiology Artificial Intelligence Workflows: Opportunities, Challenges, and Future Directions.

AJR. American journal of roentgenology·2025
Same author

BiasPruner: Mitigating bias transfer in continual learning for fair medical image analysis.

Medical image analysis·2025
Same author

SuperResNET: Model-Free Single-Molecule Network Analysis Software Achieves Molecular Resolution of Nup96.

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)·2025
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K

Probabilistic multi-shape representation using an isometric log-ratio mapping.

Neda Changizi1, Ghassan Hamarneh

  • 1Medical Image Analysis Lab, Simon Fraser University, Canada. nca19@cs.sfu.ca

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for analyzing probabilistic shapes in medical images. It uses the isometric log-ratio (ILR) transformation to ensure statistically valid shape representations, improving accuracy in shape analysis.

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

6.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Related Experiment Videos

Last Updated: May 1, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

6.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Area of Science:

  • Medical Imaging
  • Statistical Shape Analysis
  • Computational Anatomy

Background:

  • Probabilistic labeling is crucial for handling shape uncertainties in medical images.
  • Standard statistical shape analysis methods applied to probabilistic data often yield invalid shape representations due to ignoring the unit simplex geometry.
  • Existing mappings like LogOdds have limitations in analyzing compositional data.

Purpose of the Study:

  • To develop a new statistical framework for analyzing probabilistic multi-shape anatomy that is intrinsic to the unit simplex.
  • To address the limitations of applying unconstrained statistical methods to probabilistic shape data.
  • To demonstrate the superiority of the isometric log-ratio (ILR) transformation for this analysis.

Main Methods:

  • Utilized methods for compositional or closed data analysis.
  • Implemented the isometric log-ratio (ILR) transformation to map the unit simplex to Euclidean space.
  • Performed statistical analysis on transformed data and back-transformed results to the simplex.

Main Results:

  • The proposed framework ensures statistically feasible shape representations within the unit simplex.
  • Demonstrated favorable properties of the ILR transformation compared to existing mappings.
  • Achieved more accurate statistical analysis of probabilistic shapes on both synthetic and real brain imaging data.

Conclusions:

  • The ILR-based framework provides an intrinsic and accurate method for statistical analysis of probabilistic shapes.
  • This approach overcomes the limitations of traditional methods when dealing with probabilistic anatomical data.
  • The findings suggest improved accuracy and validity in medical image shape analysis.