False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.
Identifieur interne : 000460 ( Main/Exploration ); précédent : 000459; suivant : 000461False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.
Auteurs : Sami Dhahbi [Tunisie] ; Walid Barhoumi [Tunisie] ; Jaroslaw Kurek [Pologne] ; Bartosz Swiderski [Pologne] ; Michal Kruk [Pologne] ; Ezzeddine Zagrouba [Tunisie]Source :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2018.
Descripteurs français
- KwdFr :
- Arbres de décision (MeSH), Bases de données factuelles (statistiques et données numériques), Conception de logiciel (MeSH), Faux positifs (MeSH), Femelle (MeSH), Humains (MeSH), Interprétation d'images radiographiques assistée par ordinateur (méthodes), Machine à vecteur de support (MeSH), Mammographie (statistiques et données numériques), Tumeurs du sein (imagerie diagnostique).
- MESH :
- imagerie diagnostique : Tumeurs du sein.
- méthodes : Interprétation d'images radiographiques assistée par ordinateur.
- statistiques et données numériques : Bases de données factuelles, Mammographie.
- Arbres de décision, Conception de logiciel, Faux positifs, Femelle, Humains, Machine à vecteur de support.
English descriptors
- KwdEn :
- Breast Neoplasms (diagnostic imaging), Databases, Factual (statistics & numerical data), Decision Trees (MeSH), False Positive Reactions (MeSH), Female (MeSH), Humans (MeSH), Mammography (statistics & numerical data), Radiographic Image Interpretation, Computer-Assisted (methods), Software Design (MeSH), Support Vector Machine (MeSH).
- MESH :
- diagnostic imaging : Breast Neoplasms.
- methods : Radiographic Image Interpretation, Computer-Assisted.
- statistics & numerical data : Databases, Factual, Mammography.
- Decision Trees, False Positive Reactions, Female, Humans, Software Design, Support Vector Machine.
Abstract
BACKGROUND AND OBJECTIVE
The aim of computer-aided-detection (CAD) systems for mammograms is to assist radiologists by marking region of interest (ROIs) depicting abnormalities. However, the confusing appearance of some normal tissues that visually look like masses results in a large proportion of marked ROIs with normal tissues. This paper copes with this problem and proposes a framework to reduce false positive masses detected by CAD.
METHODS
To avoid the error induced by the segmentation step, we proposed a segmentation-free framework with particular attention to improve feature extraction and classification steps. We investigated for the first time in mammogram analysis, Hilbert's image representation, Kolmogorov-Smirnov distance and maximum subregion descriptors. Then, a feature selection step is performed to select the most discriminative features. Moreover, we considered several classifiers such as Random Forest, Support Vector Machine and Decision Tree to distinguish between normal tissues and masses. Our experiments were carried out on a large dataset of 10168 ROIs (8254 normal tissues and 1914 masses) constructed from the Digital Database for Screening Mammography (DDSM). To simulate practical scenario, our normal regions are false positives asserted by a CAD system from healthy cases.
RESULTS
The combination of all the descriptors yields better results than each feature set used alone, and the difference is statistically significant. Besides, the feature selection steps yields a statistically significant increase in the accuracy values for the three classifiers. Finally, the random forest achieves the highest accuracy (81.09%), outperforming the SVM classifier (80.01%)) and decision tree (79.12%), but the difference is not statistically significant.
CONCLUSIONS
The accuracy of discrimination between normal and abnormal ROIs in mammograms obtained with the proposed gray level texture features sets are encouraging and comparable to these obtained with multiresolution features. Combination of several features as well as feature selection steps improve the results. To improve false positives reduction in CAD systems for breast cancer diagnosis, these features could be combined with multiresolution features.
DOI: 10.1016/j.cmpb.2018.03.026
PubMed: 29728249
Affiliations:
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Le document en format XML
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<term>Databases, Factual (statistics & numerical data)</term>
<term>Decision Trees (MeSH)</term>
<term>False Positive Reactions (MeSH)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Mammography (statistics & numerical data)</term>
<term>Radiographic Image Interpretation, Computer-Assisted (methods)</term>
<term>Software Design (MeSH)</term>
<term>Support Vector Machine (MeSH)</term>
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<term>Bases de données factuelles (statistiques et données numériques)</term>
<term>Conception de logiciel (MeSH)</term>
<term>Faux positifs (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Interprétation d'images radiographiques assistée par ordinateur (méthodes)</term>
<term>Machine à vecteur de support (MeSH)</term>
<term>Mammographie (statistiques et données numériques)</term>
<term>Tumeurs du sein (imagerie diagnostique)</term>
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<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Interprétation d'images radiographiques assistée par ordinateur</term>
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<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Databases, Factual</term>
<term>Mammography</term>
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<keywords scheme="MESH" xml:lang="en"><term>Decision Trees</term>
<term>False Positive Reactions</term>
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<term>Humans</term>
<term>Software Design</term>
<term>Support Vector Machine</term>
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<term>Conception de logiciel</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND AND OBJECTIVE</b>
</p>
<p>The aim of computer-aided-detection (CAD) systems for mammograms is to assist radiologists by marking region of interest (ROIs) depicting abnormalities. However, the confusing appearance of some normal tissues that visually look like masses results in a large proportion of marked ROIs with normal tissues. This paper copes with this problem and proposes a framework to reduce false positive masses detected by CAD.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>To avoid the error induced by the segmentation step, we proposed a segmentation-free framework with particular attention to improve feature extraction and classification steps. We investigated for the first time in mammogram analysis, Hilbert's image representation, Kolmogorov-Smirnov distance and maximum subregion descriptors. Then, a feature selection step is performed to select the most discriminative features. Moreover, we considered several classifiers such as Random Forest, Support Vector Machine and Decision Tree to distinguish between normal tissues and masses. Our experiments were carried out on a large dataset of 10168 ROIs (8254 normal tissues and 1914 masses) constructed from the Digital Database for Screening Mammography (DDSM). To simulate practical scenario, our normal regions are false positives asserted by a CAD system from healthy cases.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>The combination of all the descriptors yields better results than each feature set used alone, and the difference is statistically significant. Besides, the feature selection steps yields a statistically significant increase in the accuracy values for the three classifiers. Finally, the random forest achieves the highest accuracy (81.09%), outperforming the SVM classifier (80.01%)) and decision tree (79.12%), but the difference is not statistically significant.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>The accuracy of discrimination between normal and abnormal ROIs in mammograms obtained with the proposed gray level texture features sets are encouraging and comparable to these obtained with multiresolution features. Combination of several features as well as feature selection steps improve the results. To improve false positives reduction in CAD systems for breast cancer diagnosis, these features could be combined with multiresolution features.</p>
</div>
</front>
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<li>Tunisie</li>
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<settlement><li>Tunis</li>
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<orgName><li>Université de Tunis - El Manar</li>
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<tree><country name="Tunisie"><region name="Gouvernorat de Tunis"><name sortKey="Dhahbi, Sami" sort="Dhahbi, Sami" uniqKey="Dhahbi S" first="Sami" last="Dhahbi">Sami Dhahbi</name>
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<name sortKey="Barhoumi, Walid" sort="Barhoumi, Walid" uniqKey="Barhoumi W" first="Walid" last="Barhoumi">Walid Barhoumi</name>
<name sortKey="Zagrouba, Ezzeddine" sort="Zagrouba, Ezzeddine" uniqKey="Zagrouba E" first="Ezzeddine" last="Zagrouba">Ezzeddine Zagrouba</name>
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<country name="Pologne"><noRegion><name sortKey="Kurek, Jaroslaw" sort="Kurek, Jaroslaw" uniqKey="Kurek J" first="Jaroslaw" last="Kurek">Jaroslaw Kurek</name>
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<name sortKey="Kruk, Michal" sort="Kruk, Michal" uniqKey="Kruk M" first="Michal" last="Kruk">Michal Kruk</name>
<name sortKey="Swiderski, Bartosz" sort="Swiderski, Bartosz" uniqKey="Swiderski B" first="Bartosz" last="Swiderski">Bartosz Swiderski</name>
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