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Authors: Fabián Narváez ; Andrea Rueda and Eduardo Romero

Affiliation: Universidad Nacional de Colombia, Colombia

Keyword(s): Breast Cancer, Mammographic Mass Classification, Sparse Representation.

Abstract: Breast mass detection and classification in mammograms is considered a very difficult task in medical image analysis. In this paper, we present a novel approach for classification of masses in digital mammograms according with their severity (benign or malign). Unlike other approaches, we do not segment masses but instead, we attempt to describe entire regions of interest (RoIs) based on a sparse representation. A set of patches selected by a radiologist in a RoI are characterized by their projection onto learned dictionaries, constructed previously from classified regions. Finally, the region class was identified using a decision rule algorithm. The strategy was assessed in a set of 80 masses with different shapes extracted from the DDSM database. The classification was compared with a ground truth already provided in the data base, showing an average accuracy rate of 70%.

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Paper citation in several formats:
Narváez, F.; Rueda, A. and Romero, E. (2011). Breast Masses Classification using a Sparse Representation. In Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems (BIOSTEC 2011) - MIAD; ISBN 978-989-8425-38-6, SciTePress, pages 26-33. DOI: 10.5220/0003304300260033

@conference{miad11,
author={Fabián Narváez. and Andrea Rueda. and Eduardo Romero.},
title={Breast Masses Classification using a Sparse Representation},
booktitle={Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems (BIOSTEC 2011) - MIAD},
year={2011},
pages={26-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003304300260033},
isbn={978-989-8425-38-6},
}

TY - CONF

JO - Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems (BIOSTEC 2011) - MIAD
TI - Breast Masses Classification using a Sparse Representation
SN - 978-989-8425-38-6
AU - Narváez, F.
AU - Rueda, A.
AU - Romero, E.
PY - 2011
SP - 26
EP - 33
DO - 10.5220/0003304300260033
PB - SciTePress

- Science and Technology Publications, Lda.
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