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Template matching by means of correlation coefficient for detecting cancerous masses in mammograms

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the authors' experiences with the detection of cancerous masses in mammograms. The described detection method is based on the use of multiscale template matching and multiresolution. As a measure of similarity, the correlation coefficient is adapted. The main conclusion drawn from the conducted experiments is that by sufficiently dense scaling of the templates one can achieve FROC (Free Response Operating Characteristics) curves of the same quality as the curves obtained in the literature with considerably more sophisticated methods. The results were calculated for full mammograms of the entire MIAS database, in contrast to the literature, where the results are often given for regions of interest or for selected images. Several options for the templates were investigated, including three variants based on the hemispherical gray level distribution, as well as the optimal choice of the increasing scale of templates covering the whole range of diameters of masses.
Rocznik
Strony
329--345
Opis fizyczny
Bibliogr. 38 poz., il., tab., wykr.
Twórcy
autor
  • Institute of Fundamental Technological Research, Swietokrzyska 21 Str., Warsaw, Poland, mbator@ippt.gov.pl
Bibliografia
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  • [17] Sahiner B., Chan H. P., Petrick N., et al.: Computerized characterization of masses on mammograms: The Rubber Band Straightening Transform and Texture Analysis, Medical Physics, 25(4), pp. 516-526, 1998.
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  • [21] Brake G. M. and Karssemeijer N.: Single and multiscale detection of masses in digital mammograms, IEEE Transactions on Medical Imaging, 18(7), pp. 628-639, 1999.
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  • [27] Zheng L. and Chan A. K.: An artificial intelligent algorithm for tumor detection in screening mammogram, IEEE Transactions on Medical Imaging, 20(7), pp. 559-567, 2001.
  • [28] Baydush A. H., Catarious D. M., Abbey C. K., et al.: Computer-assisted detection of masses in mammography using subregion hotelling observers, Medical Physics, 30(7), pp. 1781-1787, 2003.
  • [29] Tourassi G. D., Vaergas-Voracek R., Catarious D. M. et al.: Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information, Medical Physics, 30(8), pp. 2123-2130, 2003.
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  • [32] Thangavel K., Karnan M., Sivakumar R., et al.: Automatic detection of microcalcifications in mammograms - A Review, CAD System for Preprocessing and Enhancement of Digital Mammograms, ICGST International Journal on Graphics, Vision and Image Processing, 05(5), pp. 31-61, 2005.
  • [33] Bator M. and Nieniewski M.: The Usage of Template Matching and Multi-Resolution for Detecting Cancerous Masses in Mammograms, Proc. 11th Conf. Medical Informatics & Technology, Malinka, pp. 324-329, 2006.
  • [34] Roffilli M.: Advanced machine learning techniques for digital mammography, Technical Report UBLCS-2006-12, University of Bologna, Bologna, 2006.
  • [35] Bator M., Ustymowicz M., and Nieniewski M.: Experiences with detection of microcalcifications and cancereous masses in mammograms. Chapter in „Intelligent Extraction of Information for Diagnostic Purposes” (a book in Polish), PWNT, Gdansk, pp. 305-322, 2007.
  • [36] Doi K.: Computer-aided diagnosis in medical imaging: Historical Review, Current Status and Future Potential, Computerized Medical Imaging and Graphics, 31, pp. 198-211, 2007.
  • [37] Kom G., Tiedeu A., and Kom M.: Automated detection of masses in mammograms by local adaptive thresholding, Computers in Biology and Medicine, 37(1), pp. 37-48, 2007.
  • [38] Nishikawa R. M.: Current status and future directions of computer-aided diagnosis in mammography, Computerized Medical Imaging and Graphics, 31, pp. 224-235, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0031-0008
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