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Harmonic Mean

Harmonic Mean

The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...

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Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain.

Huachao Chen¹, Xinze Li¹, Ka-Hou Chan¹

¹Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Quantitative Imaging in Medicine and Surgery

|August 11, 2025

Summary

This study introduces novel singular value decomposition (SVD) algorithms for medical image harmonization. These methods improve image consistency and quality while preserving diagnostic details, enhancing machine learning performance.

Keywords:

Harmonization deep learning medical image processing robustness singular value decomposition (SVD)

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Last Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: November 27, 2017

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

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Published on: August 30, 2013

Area of Science:

Medical Imaging
Machine Learning
Image Processing

Background:

Medical imaging variations cause inconsistencies, hindering diagnostic accuracy and AI model performance.
Existing harmonization techniques often compromise critical image details.
This study addresses image variability while preserving essential diagnostic information.

Purpose of the Study:

To develop novel singular value decomposition (SVD)-based harmonization and de-harmonization algorithms.
To ensure consistency across diverse medical imaging conditions.
To preserve essential diagnostic information in medical images.

Main Methods:

Utilized SVD to decompose images into frequency bands for targeted adjustments.
Applied SVD to RGB channels for selective enhancement of relevant structures and contrast normalization.
Integrated harmonization and de-harmonization processes, validated through homology and heterology experiments on diverse datasets (MNIST, USPS, DRIVE, CHASE_DB1, RSNAbreast, INbreast).

Main Results:

SVD algorithms outperformed traditional methods in image quality and efficiency.
Achieved high accuracy in digit classification (e.g., 99.21% on MNIST).
Demonstrated strong performance in retinal vessel segmentation (AUCs up to 0.982) and breast cancer detection (AUCs up to 0.934).

Conclusions:

SVD-based algorithms offer a robust solution for medical image variability.
The techniques enhance visual quality and clinical utility across datasets and modalities.
Proven potential to improve machine learning model effectiveness in medical imaging tasks.