eprintid: 321
rev_number: 5
eprint_status: archive
userid: 5
dir: disk0/00/00/03/21
datestamp: 2011-07-12
lastmod: 2013-06-28 11:55:20
status_changed: 2013-06-28 11:55:20
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Viani, Federico
creators_name: Meaney, Paul
creators_name: Rocca, Paolo
creators_name: Azaro, Renzo
creators_name: Donelli, Massimo
creators_name: Oliveri, Giacomo
creators_name: Massa, Andrea
title: Numerical Validation and Experimental Results of a Multi-Resolution SVM-Based Classification Procedure for Breast Imaging
ispublished: pub
subjects: TU
full_text_status: public
note: This version is a pre-print of the final version available at IEEE.
abstract: X-Ray mammography is the principal technique for breast cancer screening in clinical practice. However, X-ray mammography presents different drawbacks showing that alternative technologies are desirable. For example, the difficulty in detecting the breast tumors at the earlier stage, the destructive effects of the ionizing X-rays on the irradiated tissues, low efficiency in dense breasts and with cancer near the chest wall. Other minor but still negative aspects are also the discomfort due to the breast compression and the expensive costs [1].
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] Q. H. Liu, Z. Q. Zhang, T. T. Wang, J. A. Brgan, G. A. Ybarra, L. W. Nolte, and W. T. Joines, “Active microwave imaging I - 2-D forward and inverse scattering methods,” IEEE Trans. Microwave Theory Tech., vol. 50, no. 1, pp. 123–133, Jan. 2002. [2] P. M. Meaney, M. W. Fanning, D. Li, S. Poplack, and K. D. Paulsen, “A clinical prototype for active microwave imaging of the breast,” IEEE Trans. Microw. Theory Tech., vol. 48, no. 11, pp. 1841–1853, Nov. 2000. [3] A. E. Souvorov, A. E. Bulyshev, S. Y. Semenov, R. H. Svenson, A. G. Nazarov, Y. E. Sizov, and G. P. Tatsis, “Microwave tomography: A two-dimensional newton iterative scheme,” IEEE Trans. Microw. Theory Tech., vol. 46, pp. 1654–1659, Nov. 1998. [4] S. Caorsi, A. Massa, M. Pastorino, and A. Rosani, “Microwave medical imaging: Potentialities and limitations of a stochastic optimization technique,” IEEE Trans. Microw. Theory Tech., vol. 52, no. 8, pp. 1909–1916, Aug. 2004. [5] R. Lucht, S. Delorme, and G. Brix, “Neural network-based segmentation of dynamic MR mammographic images,” Magn. Reson. Imag., vol. 20, pp. 147–154, 2002. [6] P. Meaney, S. Pendergrass, M. Fanning, D. Li, and K. Paulsen, “Importance of using a reduced contrast coupling medium in 2D microwave breast imaging,” J. Electromagn. Waves Applicat., vol. 17, no. 2, pp. 333–355, 2003. [7] A. Massa, A. Boni, and M. Donelli, “A classification approach based on SVM for electromagnetic subsurface sensing,” IEEE Trans. Geosci. Remote Sens, vol. 43, no. 9, Sept. 2005. [8] V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998. [9] S. Caorsi, M. Donelli, D. Franceschini, and A. Massa, “A new methodology based on an iterative multiscaling for microwave imaging,” IEEE Trans. Microw. Theory Tech., vol. 51, no. 4, pp. 1162-1173, Apr. 2003.
citation:   Viani, Federico and Meaney, Paul and Rocca, Paolo and Azaro, Renzo and Donelli, Massimo and Oliveri, Giacomo and Massa, Andrea  (2011) Numerical Validation and Experimental Results of a Multi-Resolution SVM-Based Classification Procedure for Breast Imaging.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/321/1/DISI-11-191.C176.pdf