Abstract
Mammography is considered the most effective method for early detection of the breast cancer. However, it is difficult for radiologists to detect microcalcification (MC) clusters and camouflages masses. The mammograms (MG) images were decomposed into several subimages using Wavelet transform (WT) based on classical and novel class Wavelets using atomic function for reducing the volume of data in the classification stage. Various regions of interest (ROIs) in the MG images were selected where input data for multilayer artificial neural network (ANN) type classifier are formed applying the WT. We used different patterns to classify the normal, MC, spiculated and circumscribed masses ROIs. The detection performance has been evaluated on MG images from the Mammographic Image Analysis Society (MIAS) database. The proposed classification scheme was shown good performance in detecting the MC clusters and masses with acceptable classification.
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Juarez-Landin, C., Ponomaryov, V., Sanchez-Ramirez, J.L., Martinez-Reyes, M., Kravchenko, V. (2008). Wavelets Based on Atomic Function Used in Detection and Classification of Masses in Mammography. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_28
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DOI: https://doi.org/10.1007/978-3-540-88636-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
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