C4.5: programs for machine learning
C4.5: programs for machine learning
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to the classification of mammographic masses and normal breast tissue
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Journal of Cognitive Neuroscience
A comparison of breast tissue classification techniques
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Computerized Detection of Pulmonary Nodule Based on Two-Dimensional PCA
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
A new GLLD operator for mass detection in digital mammograms
Journal of Biomedical Imaging - Special issue on Advanced Signal Processing Methods for Biomedical Imaging
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In this paper we present a novel method for reducing false positives in breast mass detection. Our approach is based on using the Two-Dimensional Principal Component Analysis (2DPCA) algorithm, recently proposed in the field of face recognition, in order to extract breast mass image features. In mammography, it is well known that the breast density measure is highly related to the risk of breast cancer development. Hence, we also propose to take advantage of a previous breast density classification in order to increase the overall breast mass detection performance. We test our approach using a set of 1792 RoIs manually extracted from the DDSM database. Moreover, we compare our results with several existing methods. The obtained results demonstrate the validity of our approach, not only in terms of improving the performance but being a generalizable, simple, and cost-effective approach.