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An Ultrasound Image Despeckling Approach Based on Principle Component Analysis
Jawad F. Al-Asad, Ali M. Reza, Udomchai Techavipoo
Pages - 156 - 177     |    Revised - 01-06-2014     |    Published - 01-07-2014
Volume - 8   Issue - 4    |    Publication Date - July 2014  Table of Contents
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KEYWORDS
Covariance Matrix, Denoising, Despeckling, Principle Component Analysis, Ultrasound Imaging.
ABSTRACT
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
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Dr. Jawad F. Al-Asad
Prince Mohammad Bin Fahd University - Saudi Arabia
jalasad@pmu.edu.sa
Professor Ali M. Reza
University of Wisconsin-Milwaukee - United States of America
Dr. Udomchai Techavipoo
National Electronics and Computer Technology Center - Thailand


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