WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS

We apply the 2D wavelet transform (WTMM) method to perform a multifractal analysis of digitized mammograms. We show that normal regions display monofractal scaling properties as characterized by the socalled Hurst exponent H =0.3±0.1 in fatty areas which look like antipersistent self-similar random...

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Main Authors: Pierre Kestener, Jean Marc Lina, Philippe Saint-Jean, Alain Arneodo
Format: Article
Language:English
Published: Slovenian Society for Stereology and Quantitative Image Analysis 2011-05-01
Series:Image Analysis and Stereology
Subjects:
Online Access:http://www.ias-iss.org/ojs/IAS/article/view/674
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spelling doaj-72858a529e7f499d9251afb46aa7c5e12020-11-25T02:27:14ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652011-05-0120316917410.5566/ias.v20.p169-174646WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMSPierre KestenerJean Marc LinaPhilippe Saint-JeanAlain ArneodoWe apply the 2D wavelet transform (WTMM) method to perform a multifractal analysis of digitized mammograms. We show that normal regions display monofractal scaling properties as characterized by the socalled Hurst exponent H =0.3±0.1 in fatty areas which look like antipersistent self-similar random surfaces, while H=0.65±0.1 in dense areas which exibit long-range correlations and possibly multifractal scaling properties. We further demonstrate that the 2D WTMM method provides a very efficient way to detect tumors as well as microcalcifications (MC) which correspond to much stronger singularities than those involved in the background tissue roughness fluctuations. These preliminary results indicate that the texture discriminatory power of the 2D WTMM method may lead to significant improvement in computer-assisted diagnosis in digitized mammograms.http://www.ias-iss.org/ojs/IAS/article/view/674breast tissuefractional Brownian motionsHurst exponentimage analysismammogrammicrocalcificationsmultifractal formalismrough surfacescale invariancewavelet transform
collection DOAJ
language English
format Article
sources DOAJ
author Pierre Kestener
Jean Marc Lina
Philippe Saint-Jean
Alain Arneodo
spellingShingle Pierre Kestener
Jean Marc Lina
Philippe Saint-Jean
Alain Arneodo
WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
Image Analysis and Stereology
breast tissue
fractional Brownian motions
Hurst exponent
image analysis
mammogram
microcalcifications
multifractal formalism
rough surface
scale invariance
wavelet transform
author_facet Pierre Kestener
Jean Marc Lina
Philippe Saint-Jean
Alain Arneodo
author_sort Pierre Kestener
title WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
title_short WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
title_full WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
title_fullStr WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
title_full_unstemmed WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS
title_sort wavelet-based multifractal formalism to assist in diagnosis in digitized mammograms
publisher Slovenian Society for Stereology and Quantitative Image Analysis
series Image Analysis and Stereology
issn 1580-3139
1854-5165
publishDate 2011-05-01
description We apply the 2D wavelet transform (WTMM) method to perform a multifractal analysis of digitized mammograms. We show that normal regions display monofractal scaling properties as characterized by the socalled Hurst exponent H =0.3±0.1 in fatty areas which look like antipersistent self-similar random surfaces, while H=0.65±0.1 in dense areas which exibit long-range correlations and possibly multifractal scaling properties. We further demonstrate that the 2D WTMM method provides a very efficient way to detect tumors as well as microcalcifications (MC) which correspond to much stronger singularities than those involved in the background tissue roughness fluctuations. These preliminary results indicate that the texture discriminatory power of the 2D WTMM method may lead to significant improvement in computer-assisted diagnosis in digitized mammograms.
topic breast tissue
fractional Brownian motions
Hurst exponent
image analysis
mammogram
microcalcifications
multifractal formalism
rough surface
scale invariance
wavelet transform
url http://www.ias-iss.org/ojs/IAS/article/view/674
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AT jeanmarclina waveletbasedmultifractalformalismtoassistindiagnosisindigitizedmammograms
AT philippesaintjean waveletbasedmultifractalformalismtoassistindiagnosisindigitizedmammograms
AT alainarneodo waveletbasedmultifractalformalismtoassistindiagnosisindigitizedmammograms
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