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

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

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

Sort by

Same author

Automated Multi-Modal MRI Segmentation of Stroke Lesions and Corticospinal Tract Integrity for Functional Outcome Prediction.

Tomography (Ann Arbor, Mich.)·2026

Same author

Wideband circularly polarized dielectric resonator antenna with high gain for microwave wireless power transfer.

Scientific reports·2026

Same author

S<sup>2</sup>CIE: semantic, syntactic, and context-based information extraction for AOP development.

Environment international·2025

Same author

Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.

Journal of imaging informatics in medicine·2025

Same author

Advancing breast cancer relapse prediction with radiomics and neural networks: a clinically interpretable framework.

Frontiers in oncology·2025

Same author

M-TabNet: A Transformer-Based Multi-Encoder for Early Neonatal Birth Weight Prediction Using Multimodal Data.

IEEE journal of biomedical and health informatics·2025

Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026

Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026

Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026

Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026

Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026

Same journal

Spectral super-resolution for Parkinson's voice via representation-level methods under mixed-reality acquisition.

Computer methods and programs in biomedicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 23, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Temporal mammogram image registration using optimized curvilinear coordinates.

Mohamed Abdel-Nasser¹, Antonio Moreno¹, Domenec Puig¹

¹Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, Tarragona 43007, Spain.

Computer Methods and Programs in Biomedicine

|March 23, 2016

Summary

This summary is machine-generated.

This study introduces a novel temporal mammogram registration method using curvilinear coordinates to improve breast cancer detection. The method effectively aligns mammograms, enhancing comparison for computer-aided diagnosis systems.

Keywords:

Coordinates Mammogram Mutual information Optimization Registration

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Related Experiment Videos

Last Updated: Mar 23, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Mammogram registration is crucial for accurate breast cancer detection in computer-aided diagnosis (CADx).
Comparing temporal mammograms aids radiologists in identifying subtle abnormalities.
Existing registration methods may struggle with global and local breast deformations.

Purpose of the Study:

To propose and validate a novel temporal mammogram registration method.
To address challenges posed by breast tissue deformations in mammogram alignment.
To enhance the accuracy of mammogram comparison for improved CADx.

Main Methods:

A temporal mammogram registration technique utilizing curvilinear coordinates.
The method is designed to handle both global and local deformations in the breast region.
Validation performed on temporal mammogram pairs.

Main Results:

The proposed method maximizes similarity between registered mammograms.
It significantly decreases the distance between manually defined landmarks.
Demonstrated effectiveness compared to state-of-the-art mammogram registration techniques.

Conclusions:

The developed curvilinear coordinate-based method offers effective temporal mammogram registration.
This approach shows promise for improving the performance of breast cancer CADx systems.
The method's ability to handle deformations enhances its clinical applicability.