A boosting based approach for automatic micro-calcification detection

Authors:
Arnau Oliver;Albert Torrent;Meritxell Tortajada;Xavier Lladó;Marta Peracaula;Lidia Tortajada;Melcior Sentís;Jordi Freixenet
Affiliations:
Dept Computer Architecture and Technology, University of Girona, Girona, Spain;Dept Computer Architecture and Technology, University of Girona, Girona, Spain;Dept Computer Architecture and Technology, University of Girona, Girona, Spain;Dept Computer Architecture and Technology, University of Girona, Girona, Spain;Dept Computer Architecture and Technology, University of Girona, Girona, Spain;Radiology Dept., UDIAT-Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain;Radiology Dept., UDIAT-Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain;Dept Computer Architecture and Technology, University of Girona, Girona, Spain
Venue:
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Year:
2010

Citing 2
Cited 2

Breast Skin-Line Segmentation Using Contour Growing

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques

Computers in Biology and Medicine

Automatic microcalcification and cluster detection for digital and digitised mammograms

Knowledge-Based Systems
Detecting clusters of microcalcifications with a cascade-based approach

IWDM'12 Proceedings of the 11th international conference on Breast Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a boosting based approach for automatic detection of micro-calcifications in mammographic images Our proposal is based on using local features extracted from a bank of filters for obtaining a description of the different micro-calcifications morphology The approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosting classifier to perform the detection The validity of our method is demonstrated using 112 mammograms of the well-known digitised MIAS database and 280 mammograms of a full-field digital database The experimental evaluation is performed in terms of ROC analysis, obtaining Az=0.88 and Az=0.90 respectively, and FROC analysis The obtained results show the feasibility of our approach for detecting micro-calcifications in both digitised and digital technologies.