Breast cancer diagnosis in digitized mammograms using curvelet moments.
Identifieur interne : 000283 ( Main/Exploration ); précédent : 000282; suivant : 000284Breast cancer diagnosis in digitized mammograms using curvelet moments.
Auteurs : Sami Dhahbi [Tunisie] ; Walid Barhoumi [Tunisie] ; Ezzeddine Zagrouba [Tunisie]Source :
- Computers in biology and medicine [ 1879-0534 ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
- imagerie diagnostique : Tumeurs du sein.
- méthodes : Interprétation d'images assistée par ordinateur, Mammographie.
- Algorithmes, Bases de données factuelles, Femelle, Humains, Reproductibilité des résultats.
English descriptors
- KwdEn :
- MESH :
- diagnostic imaging : Breast Neoplasms.
- methods : Image Interpretation, Computer-Assisted, Mammography.
- Algorithms, Databases, Factual, Female, Humans, Reproducibility of Results.
Abstract
BACKGROUND
Feature extraction is a key issue in designing a computer aided diagnosis system. Recent researches on breast cancer diagnosis have reported the effectiveness of multiscale transforms (wavelets and curvelets) for mammogram analysis and have shown the superiority of curvelet transform. However, the curse of dimensionality problem arises when using the curvelet coefficients and therefore a reduction method is required to extract a reduced set of discriminative features.
METHODS
This paper deals with this problem and proposes a feature extraction method based on curvelet transform and moment theory for mammogram description. First, we performed discrete curvelet transform and we computed the four first-order moments from curvelet coefficients distribution. Hence, two feature sets can be obtained: moments from each band and moments from each level. In this work, both sets are studied. Then, the t-test ranking technique was applied to select the best features from each set. Finally, a k-nearest neighbor classifier was used to distinguish between normal and abnormal breast tissues and to classify tumors as malignant or benign. Experiments were performed on 252 mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on 11553 mammograms from the Digital Database for Screening Mammography (DDSM) database using 2×5-fold cross validation.
RESULTS
Experimental results prove the effectiveness and the superiority of curvelet moments for mammogram analysis. Indeed, results on the mini-MIAS database show that curvelet moments yield an accuracy of 91.27% (resp. 81.35 %) with 10 (resp. 8) features for abnormality (resp. malignancy) detection. In addition, empirical comparisons of the proposed method against state-of-the-art curvelet-based methods on the DDSM database show that the suggested method does not only lead to a more reduced feature set, but it also statistically outperforms all the compared methods in terms of accuracy.
CONCLUSIONS
In summary, curvelet moments are an efficient and effective way to extract a reduced set of discriminative features for breast cancer diagnosis.
DOI: 10.1016/j.compbiomed.2015.06.012
PubMed: 26151831
Affiliations:
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Le document en format XML
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<term>Breast Neoplasms (diagnostic imaging)</term>
<term>Databases, Factual (MeSH)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Image Interpretation, Computer-Assisted (methods)</term>
<term>Mammography (methods)</term>
<term>Reproducibility of Results (MeSH)</term>
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<term>Bases de données factuelles (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Interprétation d'images assistée par ordinateur (méthodes)</term>
<term>Mammographie (méthodes)</term>
<term>Reproductibilité des résultats (MeSH)</term>
<term>Tumeurs du sein (imagerie diagnostique)</term>
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<keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Breast Neoplasms</term>
</keywords>
<keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Tumeurs du sein</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Interpretation, Computer-Assisted</term>
<term>Mammography</term>
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<term>Mammographie</term>
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<term>Humans</term>
<term>Reproducibility of Results</term>
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<term>Bases de données factuelles</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>Feature extraction is a key issue in designing a computer aided diagnosis system. Recent researches on breast cancer diagnosis have reported the effectiveness of multiscale transforms (wavelets and curvelets) for mammogram analysis and have shown the superiority of curvelet transform. However, the curse of dimensionality problem arises when using the curvelet coefficients and therefore a reduction method is required to extract a reduced set of discriminative features.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>This paper deals with this problem and proposes a feature extraction method based on curvelet transform and moment theory for mammogram description. First, we performed discrete curvelet transform and we computed the four first-order moments from curvelet coefficients distribution. Hence, two feature sets can be obtained: moments from each band and moments from each level. In this work, both sets are studied. Then, the t-test ranking technique was applied to select the best features from each set. Finally, a k-nearest neighbor classifier was used to distinguish between normal and abnormal breast tissues and to classify tumors as malignant or benign. Experiments were performed on 252 mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on 11553 mammograms from the Digital Database for Screening Mammography (DDSM) database using 2×5-fold cross validation.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>Experimental results prove the effectiveness and the superiority of curvelet moments for mammogram analysis. Indeed, results on the mini-MIAS database show that curvelet moments yield an accuracy of 91.27% (resp. 81.35 %) with 10 (resp. 8) features for abnormality (resp. malignancy) detection. In addition, empirical comparisons of the proposed method against state-of-the-art curvelet-based methods on the DDSM database show that the suggested method does not only lead to a more reduced feature set, but it also statistically outperforms all the compared methods in terms of accuracy.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>In summary, curvelet moments are an efficient and effective way to extract a reduced set of discriminative features for breast cancer diagnosis.</p>
</div>
</front>
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