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Updated: Jun 6, 2026

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

Fractional fourier-based filter for denoising elastograms.

Suba R Subramaniam¹, Tsz K Hon, Apostolos Georgakis

¹Division of Engineering, King's College London, WC2R 2LS, UK. suba.r.subramaniam@kcl.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

This study introduces a new denoising method for ultrasound elastography using fractional Fourier transforms to accurately estimate axial strains. The technique improves image quality by reducing noise and enhancing the contrast-to-noise ratio (CNR(e)).

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Last Updated: Jun 6, 2026

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

Area of Science:

Medical Imaging
Biomedical Engineering
Signal Processing

Background:

Ultrasound elastography estimates tissue properties from axial displacements.
Calculating axial strains amplifies noise, hindering accurate analysis.
Existing methods struggle with non-stationary displacement signals.

Purpose of the Study:

To develop a novel denoising scheme for accurate axial strain estimation in ultrasound elastography.
To address noise amplification issues inherent in displacement differentiation.
To improve the contrast-to-noise ratio (CNR(e)) of elastograms.

Main Methods:

A denoising method based on repeated filtering in fractional Fourier transform domains.
Implementation of a time-varying cutoff threshold to handle signal non-stationarities.
Utilizing a filter circuit with linear low-pass filters and fractional Fourier transforms.

Main Results:

The proposed method effectively reduces noise in axial strain calculations.
Achieved significant improvement in the contrast-to-noise ratio (CNR(e)) of elastograms.
Outperformed conventional low-pass filtering techniques in denoising displacement signals.

Conclusions:

The novel fractional Fourier transform-based denoising scheme enables accurate axial strain estimation.
This technique enhances the diagnostic quality of ultrasound elastography images.
The method offers a robust solution for analyzing non-stationary displacement signals in medical imaging.