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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
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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.
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Mr. M.A.Abdou
- Egypt
Mr. Mazhar B. Tayel
- Egypt
Mr. Azza M.Elbagoury
- Egypt


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