Article | REF: R1109 V1

Wavelet digital filtering - Applications in medical imaging

Author: Abdeldjalil OUAHABI

Publication date: June 10, 2013

You do not have access to this resource.
Click here to request your free trial access!

Already subscribed? Log in!


Overview

Français

ABSTRACT

Medical images are often affected by random noise occurring in the image acquisition, measurement and transmission process. The resolution of this problem can lead to improved diagnostic and surgical procedures. Wavelet filtering can allow for a reasonably efficient noise reduction from the viewpoint of the average square error, SNR (signal-to-noise ratio) or PSNR (peak signal-to-noise ratio) and also in terms of visual quality.

Read this article from a comprehensive knowledge base, updated and supplemented with articles reviewed by scientific committees.

Read the article

AUTHOR

  • Abdeldjalil OUAHABI: Doctor of Engineering Grenoble INP, Doctor of Science - University Professor at Polytech'Tours (University of Tours)

 INTRODUCTION

Medical imaging has revolutionized medical practice, becoming a commonly used tool in medicine. However, medical images are often affected by noise of a random nature, the source of which lies in the acquisition process. Solving this problem can lead to improved diagnoses and surgical procedures. It is therefore essential to eliminate this noise in order to enhance and recover the finest details that may be totally masked in noisy data.

The image filtering approach we propose here consists in converting the noisy image, initially observed in the spatial domain, into an image represented in the transformed domain, such as the wavelet or contourlet domain. The resulting wavelet coefficients are then compared with a fixed or adaptive threshold.

The current boom in wavelet transforms is mainly due to two specific properties resulting from decompositions on orthogonal wavelet bases: representation sparsity and the tendency to transform a stationary random process into decorrelated Gaussian sequences.

In the context of noise reduction, more commonly known as "denoising", the success of wavelet-based multiresolution analysis is precisely ensured by its decorrelation capability (separation of noise and useful signal) and by the notion of parsimony in its representation.

This parsimony is materialized by a small number of high-amplitude wavelet coefficients (or, more precisely, wavelet transform coefficients) representing the useful signal, assumed to be regular or piecewise regular. Noise, often assumed to be white and stationary, will tend to be spread over all wavelet components or coefficients.

Relying on these two properties (sparsity and decorrelation), appropriate filtering in the wavelet domain and calculation of the corresponding inverse wavelet transform will yield the denoised signal.

In general, image denoising using wavelet-based multi-resolution analysis requires a compromise between noise reduction and the preservation of significant image detail.

The performance of this filtering will be analyzed, both in terms of mean square error and signal-to-noise ratio (SNR or PSNR), and in terms of visual quality in the case of images.

This article is a follow-up to [RE 1 018] "Wavelet-based digital filtering – Fundamentals".

You do not have access to this resource.

Exclusive to subscribers. 97% yet to be discovered!

You do not have access to this resource.
Click here to request your free trial access!

Already subscribed? Log in!


The Ultimate Scientific and Technical Reference

A Comprehensive Knowledge Base, with over 1,200 authors and 100 scientific advisors
+ More than 10,000 articles and 1,000 how-to sheets, over 800 new or updated articles every year
From design to prototyping, right through to industrialization, the reference for securing the development of your industrial projects

KEYWORDS

denoising   |   wavelets   |   multiresolution   |   implementation   |   wavelets   |   electronics   |   biomedical   |   telecommunications   |   medical imaging   |   systems   |   measurements   |   random signal analysis   |   image processing


This article is included in

Signal processing and its applications

This offer includes:

Knowledge Base

Updated and enriched with articles validated by our scientific committees

Services

A set of exclusive tools to complement the resources

Practical Path

Operational and didactic, to guarantee the acquisition of transversal skills

Doc & Quiz

Interactive articles with quizzes, for constructive reading

Subscribe now!

Ongoing reading
Wavelet-based digital filtering