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

Limiting Reactant02:27

Limiting Reactant

70.1K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
70.1K
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Contact Angle01:13

Contact Angle

21.1K
When a solid is dipped inside a liquid, the liquid surface becomes curved near the contact. For some solid–liquid interfaces, the liquid is pulled up along the solid, while for others, the liquid surface is convex or depressed near the solid surface. This phenomenon can be explained using the concept of cohesive and adhesive forces.
The adhesive force is the molecular force between molecules of different materials, that is, between the molecules of the solid and the liquid. The cohesive...
21.1K
The Number e as a Limit01:29

The Number e as a Limit

92
The number e is a fundamental constant in calculus, playing a central role in describing continuous change, particularly exponential growth. It is most naturally defined through its relationship with the natural logarithm, which is the inverse of the exponential function with base e. This relationship allows e to be characterized using basic principles of differentiation rather than as an arbitrary numerical constant.A key property of the natural logarithm function, ln x, is that its derivative...
92
Machines: Problem Solving II01:30

Machines: Problem Solving II

672
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
672
Types of Limits I01:23

Types of Limits I

194
Limits are a key mathematical concept for understanding how functions behave as their input approaches specific values, particularly when the function is undefined. They help reveal trends and discontinuities by examining the values a function approaches rather than its actual value.One-sided limits focus on the direction from which a value is approached. When a function behaves differently depending on whether the input approaches from the left or the right, the two one-sided limits may not...
194

You might also read

Related Articles

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

Sort by
Same author

Pushing the boundaries of robotic computed tomography: automated twin-robot CT scan with maximum reachability.

Scientific reports·2026
Same author

Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration.

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

Synthesizing vocal tract magnetic resonance imaging sequences with phoneme-aware diffusion models.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Illuminating the black box of reservoir computing.

Scientific reports·2026
Same author

WeakMitoSAM: competitive prompt aggregation for point-supervised mitochondria segmentation in electron microscopy images.

Biomedical optics express·2026
Same author

Drugst.One DREAM-Drug repurposing through expert annotation and modification.

British journal of pharmacology·2026
Same journal

ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.

International journal of computer assisted radiology and surgery·2026
Same journal

Lean Unet: a compact model for image segmentation.

International journal of computer assisted radiology and surgery·2026
Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026
Same journal

Vascular geometry characterization for AI-based endovascular navigation.

International journal of computer assisted radiology and surgery·2026
Same journal

Nail It! A learning framework for autonomous surgical suturing and teleoperation on the dVRK.

International journal of computer assisted radiology and surgery·2026
Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.7K

Traditional machine learning for limited angle tomography.

Yixing Huang1, Yanye Lu2, Oliver Taubmann3,4,5

  • 1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. yixing.yh.huang@fau.de.

International Journal of Computer Assisted Radiology and Surgery
|August 24, 2018
PubMed
Summary
This summary is machine-generated.

This study shows that the reduced-error pruning tree (REPTree) machine learning model effectively reduces artifacts in limited angle tomography. Specific features like Mean-Variation-Median (MVM) and Hessian improve artifact prediction accuracy.

Keywords:
Decision treeLimited angle tomographyMachine learning

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Related Experiment Videos

Last Updated: Feb 6, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Area of Science:

  • Medical Imaging
  • Computational Science
  • Machine Learning

Background:

  • Limited angle tomography often suffers from artifacts due to incomplete data.
  • Traditional machine learning with hand-crafted features offers a potential solution for artifact reduction.

Purpose of the Study:

  • Investigate the effectiveness of traditional machine learning regression models for artifact reduction in limited angle tomography.
  • Evaluate various hand-crafted features and regression algorithms for predicting and reducing image artifacts.

Main Methods:

  • Extracted Mean-Variation-Median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features from reconstructed images.
  • Applied linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) models to predict artifact images.

Main Results:

  • REPTree achieved the lowest root-mean-square error (RMSE) of 29 HU on the Shepp-Logan phantom in parallel-beam studies.
  • MVM and Hessian features showed complementary benefits, while Laplacian was redundant with MVM.
  • SVDL features were beneficial in fan-beam reconstruction.
  • Preliminary clinical data showed REPTree could reduce some artifacts, but further improvements are needed.

Conclusions:

  • REPTree demonstrated superior performance in learning artifacts compared to LR and MLP for limited angle tomography.
  • MVM, Hessian, and SVDL features are valuable for artifact prediction in this imaging modality.
  • Further feature investigation is required for successful clinical application of REPTree.