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Warianty tytułu
Języki publikacji
Abstrakty
In this paper two multiresolution transforms (discrete 2D wavelets and complex wavelets) are compared for their capabilities to enhance local texture orientation of mammograms. The local orientation of image texture is useful feature to detect one of the typical types of abnormal findings in mammography - architectural distortions. Our research was directed to define an effective, more reliable directional model of local directional findings in mammograms. Computer-aided diagnosis was considered as a concept of accurate model application.
Rocznik
Tom
Strony
183--190
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
- Division of Telemedicine, Institute of Radioelectronics, Warsaw University of Technology, Nowowiejska 15/19 Street, Warsaw, Poland
autor
Bibliografia
- [1] Amerciacan College Of Radiology, Breast Imaging Reporting and Data System (BI-RADS), 3rd edition, 1998.
- [2] AYRES F.J., LEO DESAUTELS J.E., PRAJNA S., RANGAYYAN R.M., Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension and texture analysis. Int J CARS, Springer, 2008.
- [3] BIRDWELL R.L., BANDODKAR P., IKEDA D.M., Computer-aided Detection with Screening Mammograpy in a University Hospital Setting, Radiology, Vol. 236, No.2, 2005, pp. 451 - 457.
- [4] BOVIK A.C., MARKEY M.K., SAMPAT M.P., WHITMAN G.J., Evidence based detection of speculated masses and architectural distortions, SPIE, Medical Imaging: Image Processing, Vol. 5747, 2005, pp. 26-37.
- [5] CHAN W.L., CHOI H., BARANIUK R.G., Coherent multiscale image processing using dual-tree quaternion wavelets. IEEE Transaction on Image Processing 17(7), 2008, pp. 1069-82.
- [6] DO M.N., VETTERLI M., The Contourlet Transform: An efficient Directional Multiresolution Image Representation, IEEE Transactions on Image Processing, Vol. 14, No. 12, 2005, pp. 2091-2106.
- [7] ENDO T., FUJITA H., HARA T., ICHIKAWA T., IWASE T., MATSUBARA T., Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis, Medical Imaging: Image Processing, Vol. 5370, 2004.
- [8] JASIONOWSKA M., PRZELASKOWKSKI A., RUTCZYŃSKA A, WRÓBLEWSKA A., A two – step metod for detection of architectural distortions in mammograms. In: PIĘTKA E., KAWA J. (eds.), Information Technologies in Biomedicine, AISC 69, Springer, Heidelberg, 2010, pp. 73–84.
- [9] JASIONOWSKA M, PRZELASKOWKSKI A., JÓŹWIAK R., Characteristics of Architectural Distortions in Mammograms - Extraction of Texture Orientation with Gabor Filters, L. BOLC et al. (Eds.), ICCVG 2010, Part I, LNCS 6374, Springer-Verlag, Berlin Heidelberg, 2010, pp. 420–430.
- [10] KINGSBURY N.G., The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement, Proc. European Signal Processing Conference, EUSIPCO 98, Rhodes, September 1998, pp 319-322.
- [11] MAJUMDAR A., BHATTACHATYA A., A Comparative study in wavelets, curvelets and contourlets as feature sets for pattern recognition, The International Arab Journal of Information Technology, Vol.6, No.1, 2009, pp. 47-51.
- [12] MUSOKO V., PROCHAZKA A., Complex wavelet transform in signal and image analysis, Proc. Of 6th Int. Sc.-Techn. Conference Process Control, 2004.
- [13] University Of South Florida, Digital Database for Screening Mammography (DDSM), Florida, USA, http://marathon.csee.usf.edu./Mammography/Database.html.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0020