@inproceedings{1677e1c16ac84906b5e13101d4c9dc72,
title = "Pectoral muscle segmentation in mammograms based on cartoon-texture decomposition",
abstract = "Pectoral muscle segmentation on medio-lateral oblique views of mammograms represents an important preprocessing step in many mammographic image analysis tasks. Although its location can be perceptually obvious for a human observer, the variability in shape, size, and intensities of the pectoral muscle boundary turns its automatic segmentation into a challenging problem. In this work we propose to decompose the input mammogram into its textural and structural components at different scales prior to dynamically thresholding it into several levels. The resulting segmentations are refined with an active contour model and merged together by means of a simple voting scheme to remove possible outliers. Our method performs well compared to several other state-ofthe- art techniques. An average DICE similarity coefficient of 0.91 and mean Hausdorff distance of 3.66 ± 3.23 mm. validate our approach.",
keywords = "Breast cancer detection, Computer-aided diagnosis, Mammographic image analysis, Pectoral muscle segmentation",
author = "Adrian Galdran and Artzai Pic{\'o}n and Estibaliz Garrote and David Pardo",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015 ; Conference date: 17-06-2015 Through 19-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19390-8_66",
language = "English",
isbn = "978-3-319-19390-8",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "587--594",
editor = "Cardoso, {Jaime S.} and Roberto Paredes and Pardo, {Xos{\'e} M.}",
booktitle = "Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings",
address = "Germany",
}
