Mammographic mass segmentation using fuzzy contours.
Identifieur interne : 000324 ( Main/Corpus ); précédent : 000323; suivant : 000325Mammographic mass segmentation using fuzzy contours.
Auteurs : Marwa Hmida ; Kamel Hamrouni ; Basel Solaiman ; Sana BoussettaSource :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2018.
English descriptors
- KwdEn :
- Breast Neoplasms (diagnostic imaging), Databases, Factual (statistics & numerical data), Diagnosis, Computer-Assisted (methods), Diagnosis, Computer-Assisted (statistics & numerical data), Female (MeSH), Fuzzy Logic (MeSH), Humans (MeSH), Mammography (methods), Mammography (statistics & numerical data), Radiographic Image Interpretation, Computer-Assisted (methods), Radiographic Image Interpretation, Computer-Assisted (statistics & numerical data).
- MESH :
- diagnostic imaging : Breast Neoplasms.
- methods : Diagnosis, Computer-Assisted, Mammography, Radiographic Image Interpretation, Computer-Assisted.
- statistics & numerical data : Databases, Factual, Diagnosis, Computer-Assisted, Mammography, Radiographic Image Interpretation, Computer-Assisted.
- Female, Fuzzy Logic, Humans.
Abstract
BACKGROUND AND OBJECTIVE
Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision.
METHODS
In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass.
RESULTS
The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%.
CONCLUSIONS
The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.
DOI: 10.1016/j.cmpb.2018.07.005
PubMed: 30195421
Links to Exploration step
pubmed:30195421Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Mammographic mass segmentation using fuzzy contours.</title>
<author><name sortKey="Hmida, Marwa" sort="Hmida, Marwa" uniqKey="Hmida M" first="Marwa" last="Hmida">Marwa Hmida</name>
<affiliation><nlm:affiliation>Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie; IMT Atlantique, ITI Laboratory, Brest 29238, France. Electronic address: marwa.hmida@telecom-bretagne.eu.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Hamrouni, Kamel" sort="Hamrouni, Kamel" uniqKey="Hamrouni K" first="Kamel" last="Hamrouni">Kamel Hamrouni</name>
<affiliation><nlm:affiliation>Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie. Electronic address: kamel.hamrouni@gmail.com.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Solaiman, Basel" sort="Solaiman, Basel" uniqKey="Solaiman B" first="Basel" last="Solaiman">Basel Solaiman</name>
<affiliation><nlm:affiliation>IMT Atlantique, ITI Laboratory, Brest 29238, France. Electronic address: basel.solaiman@telecom-bretagne.eu.</nlm:affiliation>
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<author><name sortKey="Boussetta, Sana" sort="Boussetta, Sana" uniqKey="Boussetta S" first="Sana" last="Boussetta">Sana Boussetta</name>
<affiliation><nlm:affiliation>Regional hospital of Ben Arous, Tunisia. Electronic address: sana_boussetta@yahoo.fr.</nlm:affiliation>
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<author><name sortKey="Hmida, Marwa" sort="Hmida, Marwa" uniqKey="Hmida M" first="Marwa" last="Hmida">Marwa Hmida</name>
<affiliation><nlm:affiliation>Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie; IMT Atlantique, ITI Laboratory, Brest 29238, France. Electronic address: marwa.hmida@telecom-bretagne.eu.</nlm:affiliation>
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<author><name sortKey="Hamrouni, Kamel" sort="Hamrouni, Kamel" uniqKey="Hamrouni K" first="Kamel" last="Hamrouni">Kamel Hamrouni</name>
<affiliation><nlm:affiliation>Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie. Electronic address: kamel.hamrouni@gmail.com.</nlm:affiliation>
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<author><name sortKey="Solaiman, Basel" sort="Solaiman, Basel" uniqKey="Solaiman B" first="Basel" last="Solaiman">Basel Solaiman</name>
<affiliation><nlm:affiliation>IMT Atlantique, ITI Laboratory, Brest 29238, France. Electronic address: basel.solaiman@telecom-bretagne.eu.</nlm:affiliation>
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<author><name sortKey="Boussetta, Sana" sort="Boussetta, Sana" uniqKey="Boussetta S" first="Sana" last="Boussetta">Sana Boussetta</name>
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<series><title level="j">Computer methods and programs in biomedicine</title>
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<imprint><date when="2018" type="published">2018</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Breast Neoplasms (diagnostic imaging)</term>
<term>Databases, Factual (statistics & numerical data)</term>
<term>Diagnosis, Computer-Assisted (methods)</term>
<term>Diagnosis, Computer-Assisted (statistics & numerical data)</term>
<term>Female (MeSH)</term>
<term>Fuzzy Logic (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Mammography (methods)</term>
<term>Mammography (statistics & numerical data)</term>
<term>Radiographic Image Interpretation, Computer-Assisted (methods)</term>
<term>Radiographic Image Interpretation, Computer-Assisted (statistics & numerical data)</term>
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<keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Breast Neoplasms</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Diagnosis, Computer-Assisted</term>
<term>Mammography</term>
<term>Radiographic Image Interpretation, Computer-Assisted</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Databases, Factual</term>
<term>Diagnosis, Computer-Assisted</term>
<term>Mammography</term>
<term>Radiographic Image Interpretation, Computer-Assisted</term>
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<keywords scheme="MESH" xml:lang="en"><term>Female</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND AND OBJECTIVE</b>
</p>
<p>Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.</p>
</div>
</front>
</TEI>
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<ArticleTitle>Mammographic mass segmentation using fuzzy contours.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND AND OBJECTIVE" NlmCategory="OBJECTIVE">Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass.</AbstractText>
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<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.</AbstractText>
<CopyrightInformation>Copyright © 2018 Elsevier B.V. All rights reserved.</CopyrightInformation>
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<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hmida</LastName>
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