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

You might also read

Related Articles

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

Sort by

Same author

CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture.

Journal of imaging·2025

Same author

Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.

Bioengineering (Basel, Switzerland)·2025

Same author

Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches.

Bioengineering (Basel, Switzerland)·2025

Same author

QRNet: A Quaternion-Based Retinex Framework for Enhanced Wireless Capsule Endoscopy Image Quality.

Bioengineering (Basel, Switzerland)·2025

Same author

Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification.

Entropy (Basel, Switzerland)·2024

Same author

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality.

Heliyon·2024

Same journal

Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

International journal of biomedical imaging·2026

Same journal

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026

Same journal

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

International journal of biomedical imaging·2026

Same journal

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

International journal of biomedical imaging·2026

Same journal

Personalized PET Imaging in Gastric Cancer: An Umbrella Review of Meta-Analyses to Guide Radiopharmaceutical Selection and Clinical Indication.

International journal of biomedical imaging·2026

Same journal

Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

International journal of biomedical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 25, 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

Nonreference medical image edge map measure.

Karen Panetta¹, Chen Gao¹, Sos Agaian²

¹Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.

International Journal of Biomedical Imaging

|August 19, 2014

Summary

This summary is machine-generated.

A new nonreference edge measure (NREM) evaluates medical image edge maps without ground truth. This method reconstructs images from edge maps, ensuring optimal edge detection for improved diagnostic accuracy.

More Related Videos

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Related Experiment Videos

Last Updated: Apr 25, 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

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Area of Science:

Medical Image Processing
Computer Vision
Biomedical Engineering

Background:

Edge detection is crucial for medical image analysis, aiding segmentation and diagnosis.
Inaccurate edge maps can lead to errors in cancer detection algorithms.
Reference-based edge evaluation methods are unsuitable for medical imaging due to the lack of ground truth.

Purpose of the Study:

To propose a nonreference edge map evaluation method for medical images.
To enable selection of optimal edge detection algorithms and parameters without ground truth.
To provide a reliable measure for assessing edge map quality in clinical applications.

Main Methods:

A novel nonreference reconstruction-based edge map evaluation (NREM) method is introduced.
NREM assesses edge map quality by reconstructing the original image and measuring similarity.
The core principle is that a good edge map preserves image structure and details for accurate reconstruction.

Main Results:

Experimental results demonstrate NREM's effectiveness in evaluating edge maps.
Quantitative evaluations from NREM show strong correlation with human visual assessment.
The proposed method successfully aids in selecting optimal edge detection algorithms and parameters.

Conclusions:

NREM offers a viable solution for evaluating edge maps in medical imaging where ground truth is unavailable.
This nonreference approach enhances the reliability of feature extraction and segmentation in diagnostic tools.
NREM facilitates the development of more accurate and robust medical image analysis systems.