Analysis o f the Application o f Bi-Orthogonal Wavelet Transform, Bayesthresholding and Independent Component Analysis (ICA) on Poisson Noise Removal from X-Ray Images
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Date
2011
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Abstract
Medical field produces huge volume of images, which
are used during disease diagnosis. X-Rays are the oldest and most
frequently used form of medical imaging. These images are used in
many applications with prominent use found in fracture detection.
The X-Ray images are normally affected by Poisson noise, which
degrades the visual quality of the image and obscures important
information required for accurate diagnosis. The current need is, thus,
a method that removes noise while preserving important diagnostic
data. This study proposed a method that combines Multiple Wavelet
Denoising (MWD) Structure with ICA to remove Poisson noise from
X-Ray images. The thresholding method used is BayesShrink and
both soft, hard thresholding methods are analyzed. From the
experimental results, it is evident that the proposed model produces
images, which are visually clean and smooth, in fast manner. At the
same time, the proposed method also preserves edges and other
significant details of the image.