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Related Experiment Video

Updated: Jan 22, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising.

Houqiang Yu^1,2, Mingyue Ding¹, Xuming Zhang³

¹Department of Biomedical Engineering, School of Life Science and Technology, Ministry of Education Key Laboratory of Molecular Biophysics, Huazhong University of Science and Technology, No 1037, Luoyu Road, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|July 4, 2019

Summary

This summary is machine-generated.

This study introduces an improved nonlocal means (NLM) method using a novel Laplacian eigenmaps network (LEPNet) for enhanced Rician noise removal in magnetic resonance (MR) images, significantly improving detail preservation.

Keywords:

Laplacian eigenmaps MR images Rician noise convolutional neural network nonlocal means

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Area of Science:

Medical Imaging
Image Processing
Artificial Intelligence

Background:

Magnetic resonance (MR) images are susceptible to Rician noise, compromising diagnostic accuracy.
Nonlocal means (NLM) is effective for MR image denoising but struggles with detail preservation.
Existing NLM methods rely on limited information (gray-level or hand-crafted features) for patch similarity assessment.

Purpose of the Study:

To propose an improved nonlocal means (NLM) method for Rician noise removal in MR images.
To enhance the accuracy and detail preservation of MR image denoising.
To leverage intrinsic image features for more robust similarity measures in NLM.

Main Methods:

A novel Laplacian eigenmaps network (LEPNet) was developed to extract intrinsic features from pre-denoised MR images.
These extracted features were used to compute refined patch similarity measures within the NLM framework.
A post-processing step utilizing the denoised image's noise characteristics was implemented to further refine the denoising performance.

Main Results:

The proposed method demonstrated superior Rician noise removal compared to existing NLM techniques.
Enhanced preservation of image details and structural information was observed.
Objective metrics (PSNR, SSIM) and subjective visual assessment confirmed the method's effectiveness on phantom and real MR images.

Conclusions:

The integration of LEPNet-extracted features significantly improves NLM-based Rician noise reduction in MR images.
The proposed method offers a promising approach for high-fidelity MR image denoising, crucial for accurate medical diagnosis.
This technique effectively balances noise suppression with the preservation of critical image structures.