Evidence map›Paper›PMID 21286492›Full record

ArticleWorld journal of radiology2011

Content-based image retrieval applied to BI-RADS tissue classification in screening mammography.

Júlia Epischina Engrácia de Oliveira, Arnaldo de Albuquerque Araújo, Thomas M Deserno

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Article in World journal of radiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Júlia Epischina Engrácia de OliveiraJúlia Epischina Engrácia de Oliveira, Arnaldo de Albuquerque Araújo, Department of Computer Science, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil.
Arnaldo de Albuquerque Araújo
Thomas M Deserno

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo present a content-based image retrieval (CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.

methodsBreast density is characterized by image texture using singular value decomposition (SVD) and histograms. Pattern similarity is computed by a support vector machine (SVM) to separate the four BI-RADS tissue categories. The crucial number of remaining singular values is varied (SVD), and linear, radial, and polynomial kernels are investigated (SVM). The system is supported by a large reference database for training and evaluation. Experiments are based on 5-fold cross validation.

resultsAdopted from DDSM, MIAS, LLNL, and RWTH datasets, the reference database is composed of over 10 000 various mammograms with unified and reliable ground truth. An average precision of 82.14% is obtained using 25 singular values (SVD), polynomial kernel and the one-against-one (SVM).

conclusionBreast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.

Indexed as

Computer-aided diagnosisContent-based image retrievalImage processingScreening mammographySingular value decompositionSupport vector machine

Identifiers

PMID21286492
PMCPMC3030724

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