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

Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.8K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
3.8K
Poisson Probability Distribution01:09

Poisson Probability Distribution

11.1K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
11.1K
Reducing Line Loss01:18

Reducing Line Loss

253
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
253
Poisson's Ratio01:23

Poisson's Ratio

751
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
751
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.4K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.4K

You might also read

Related Articles

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

Sort by
Same author

In vivo assessment of safety, biodistribution, and radiation dosimetry of the [<sup>18</sup>F]Me4FDG PET-radiotracer in adults.

EJNMMI research·2024
Same author

Internal dosimetry study of [<sup>82</sup>Rb]Cl using a long axial field-of-view PET/CT.

European journal of nuclear medicine and molecular imaging·2024
Same author

Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

Biomedical physics & engineering express·2023
Same author

Correction: Comparison of deep learning-based denoising methods in cardiac SPECT.

EJNMMI physics·2023
Same author

Manifestations of Intellectual Disability, Dystonia, and Parkinson's Disease in an Adult Patient with <i>ARX</i> Gene Mutation c.558_560dup p.(Pro187dup).

Case reports in genetics·2023
Same author

Comparison of deep learning-based denoising methods in cardiac SPECT.

EJNMMI physics·2023

Related Experiment Video

Updated: Nov 18, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Edge-preserving adaptive autoregressive model for Poisson noise reduction.

Reijo Takalo1, Heli Hytti2, Heimo Ihalainen3

  • 1Division of Nuclear Medicine, Department of Diagnostic Radiology, Oulu University Hospital, Oulu.

Nuclear Medicine Communications
|February 9, 2021
PubMed
Summary

This study introduces an improved autoregressive model for image processing, enhancing edge sharpness and reducing Poisson noise in medical images.

More Related Videos

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.6K

Related Experiment Videos

Last Updated: Nov 18, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.6K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Signal Processing

Background:

  • Autoregressive models are linear prediction tools used in image processing.
  • These models separate images into filtered and prediction error components, highlighting edges.
  • Spatially varying modeling is a key feature of these methods.

Purpose of the Study:

  • To propose an improved autoregressive model for image processing.
  • To enhance image sharpness around edges.
  • To reduce Poisson noise in medical images, particularly nuclear medicine scans.

Main Methods:

  • Utilizing an improved autoregressive model with spatially varying capabilities.
  • Focusing on edge preservation techniques within the model.
  • Implementing noise reduction strategies specifically for Poisson noise.

Main Results:

  • The proposed model successfully preserves image sharpness at edges.
  • Significant reduction in Poisson noise was achieved in simulated and real medical images.
  • The method demonstrates effectiveness in enhancing the quality of nuclear medicine images.

Conclusions:

  • The improved autoregressive model offers a robust solution for edge preservation and noise reduction in medical imaging.
  • This approach is particularly beneficial for nuclear medicine applications where image quality is critical.
  • Further research can explore advanced spatially varying techniques for even greater noise suppression.