Mammographic Density Classification based on Local Histograms

Rafael Llobet, Juan A. Solves, Juan C. Perez-Cortes, Francisco Ruiz-Perales

2009

Abstract

In this work, the task of classifying mammograms according to breast density is studied using a local-histogram-based feature extraction method and a non-parametric classification scheme. Breast density estimation is important due to its association with a higher risk of cancer and an increased difficulty of diagnosis. 322 images from the Mammographic Image Analysis Society (MIAS) Database have been analyzed, and the density prediction accuracy of the method has been assessed. The obtained results show an agreement of 77.96% between automatic and expert radiologist manual classification.

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Paper Citation


in Harvard Style

Llobet R., Solves J., Perez-Cortes J. and Ruiz-Perales F. (2009). Mammographic Density Classification based on Local Histograms . In Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009) ISBN 978-989-8111-77-7, pages 85-90. DOI: 10.5220/0001813600850090


in Bibtex Style

@conference{workshop miad09,
author={Rafael Llobet and Juan A. Solves and Juan C. Perez-Cortes and Francisco Ruiz-Perales},
title={Mammographic Density Classification based on Local Histograms},
booktitle={Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009)},
year={2009},
pages={85-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001813600850090},
isbn={978-989-8111-77-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009)
TI - Mammographic Density Classification based on Local Histograms
SN - 978-989-8111-77-7
AU - Llobet R.
AU - Solves J.
AU - Perez-Cortes J.
AU - Ruiz-Perales F.
PY - 2009
SP - 85
EP - 90
DO - 10.5220/0001813600850090