Abstract
Although screening mammography is widely used for the detection of breast tumors, it is difficult for a radiologist to interpret correctly a mammogram. It is possible to improve this task by using a computer aided diagnosis system (CAD) which highlights the areas most likely to contain cancer cells. In this paper, we present and compare five different feature extraction methods for breast cancer detection in digitized mammograms. All the methods are based on features extracted from a local window and on a k-nearest neighbor classifier with fast search.
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Llobet, R., Paredes, R., Pérez-Cortés, J.C. (2005). Comparison of Feature Extraction Methods for Breast Cancer Detection. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_61
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DOI: https://doi.org/10.1007/11492542_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
Online ISBN: 978-3-540-32238-2
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