Home   >   CSC-OpenAccess Library   >    Manuscript Information
An Efficient Thresholding Neural Network Technique for High Noise Densities Environments
M.A.Abdou, Mazhar B. Tayel, Azza M.Elbagoury
Pages - 403 - 416     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
MORE INFORMATION
KEYWORDS
Thresholding Neural Networks, Image Denoising, High Noise Environments, Wavelet Shrinkage
ABSTRACT
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
Mehdi Nasri, Hossein Nezamabadi-pour." Image denoising in the wavelet domain using a new adaptive thresholding function". Elsevier Journal of Neurocomputing, Vol.72, 1012- 1025, 2009.
Chui, C.K.. "An introduction to wavelets". Boston (MA), Academic Press, 1992.
D.L. Donoho, I.M. Johnstone. "Adapting to unknown smoothness via wavelet shrinkage". Journal of American statistical assoc., Vol. 90, no. 432, 1200-1224, 1995.
D.L. Donoho, I.M. Johnstone. "Ideal spatial adaptation via wavelet shrinkage". Biometrica, Vol. 81, 425-455, 1994.
D.L. Donoho, I.M. Johnstone." Wavelet shrinkage: Asymptopia". J.R. Stat. Soc., series B, Vol. 57, no. 2, 301-369, 1995.
D.L. Donoho. "De-noising by soft thresholding". IEEE Trans. on Info. Theory, 933-936, 1993.
Dr. H. B. Kekre et. al.."Detection Of Tumor In MRI Using Vector Quantization Segmentation". International Journal of Engineering Science and Technology, Vol. 2, no.8, 3753-3757, 2010.
G.Sambasiva Rao,C. NagaRaju,Dr.L.S.S. Reddy and Dr.E.V. Prasad."A Novel Thresholding Technique for Adaptive Noise Reduction using Neural Networks". IJCSNS International Journal of Computer Science and Network Security, Vol.8, No.12, 315-320, December 2008.
Hernandez E., Weiss G.. "A Course on Wavelets", CRC Press, Boca Raton, 189p, 1996.
I. Daubechies."Ten Lectures on Wavelets". CBMS-NSF Regional Conference Series in Applied Mathematics, no. 61, SIAM, Philadelphia, PA, 1992.
I.N. Bankman, T. Nizialek, I. Simon, O.B Gatewood, I.N. Weinberg, W.R. Brody. "Segmentation algorithms for detecting microcalcifications in mammograms". IEEE Trans. Inform. Techn. Biomed., Vol. 1, no.2, 161-173, 1993.
Isaac N. Bankman ."Handbook of Medical Imaging". Academic Press, 2000.
J.S.Weszka."A Survey of threshold selection techniques". Computer Graphics and Image Proc., Vol.7, 259-265, 1978.
M.K. Mihcak, I. Kozintsev, K. Ramchandran, P. Moulin." Low-complexity image denoising based on statistical modeling of wavelet coefficients". IEEE Signal Process, Vol.6, 300303, 1999.
M.S. Crouse, R.D. Nowak, R.G. Baraniuk. "Wavelet-based signal processing using hidden Markov models". IEEE Trans. Signal Process.,Vol.46,886902, 1998.
M.S. Crouse, R.D. Nowak, R.G. Baraniuk. "Wavelet-based signal processing using hidden Markov models".IEEE Trans. Signal Process, Vol.46, 886902, 1998.
M.Suganthi and M.Madheswaran."A Novel Approach towards Segmentation of Breast Tumors from Screening Mammograms for Efficient Decision Support System". World Academy of Science Engineering and Technology, Vol.64, 398-403, 2010.
Mallat, S.." A wavelet tour of signal processing". San Diego (CA) Academic Press, 1999.
Nobuyuki Otsu." A Threshold Selection Method From Gray-Level Histograms ".IEEE transacnons on systems, man, and cybernencs, Vol. smc-9, no. 1, January 1979.
Olivier Rioul and Pierre Duhamel."Fast Algorithms for Discrete and Continuous Wavelet Transforms".IEEE transactions on information theory, Vol.38, no. 2, march 1992.
R.C. Gonzalez, R.E. Woods. "Digital Image Processing", Prentice-Hall, Inc. second ed., 2002.
R.M. Haralick, L.G. Shapiro. "Survey: Image segmentation techniques," Comp. Vision Graph Image Proc., Vol. 29, 100-132, 1985.
S. Chang, B. Yu, M. Vetterli. "Adaptive wavelet thresholding for image denoising and compression". IEEE Trans. Image Process, Vol.9, 15321546, 2000.
S. Haykin, Prentice-Hall. NJ."Neural Network: A Comprehensive Foundation", second ed., 1999.
S.Bernsen. "Segmentation tools in mathematical morphology". SPIE, Image algebra and morphology- ical image processing, vol. 1350, 70-84, 1990.
X.-P. Zhang, M.D. Desai." Adaptive denoising based on SURE risk". IEEE Signal Process, Vol. 5, no.10265267, 1998.
X.-P. Zhang. "Thresholding neural network for adaptive noise reduction". IEEE Trans. Neural Networks,Vol. 12,no. 3, 567584, 2001.
X.-P. Zhang." Space-scale adaptive noise reduction in images based on thresholding neural networks". Proceedings of IEEE International Con- ference on Acoustics, Speech, and Signal Processing, 18891892, 2001.
Xiao-Ping Zhang and M.Desai."Nonlinear Adaptive Noise Suppression Based On Wavelet Transform". Proc. of ICASSP'98, Seattle, Washington, May 12-15, 1998.
Mr. M.A.Abdou
- Egypt
Mr. Mazhar B. Tayel
- Egypt
Mr. Azza M.Elbagoury
- Egypt