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| Research on Equivalence of SVD and PCA in Medical Image Tilt Correction |
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Abstract In the process of medical imaging, often because of some
disturbance, the medical images frequently have some undesirable
tilt, which has costly negative effect on the following image
alignment and fusion. In order to solve the tilt problem, Singular
Value Decomposition (SVD) and Principal Component Analysis (PCA) are
studied and their relationship between them is discussed, and then
the medical image correction tilt process is divided into five main
stages. Among these stages, the key tasks focus on finding the
centroid and obtaining the tilt angle of a medical image. We use SVD
and PCA to compute the eigenvectors of the coordinates of a medical
image respectively to get the tilt angle. The experimental results
reveal that the methods mentioned above are effective for correcting
the tilt medical images and also prove the equivalence of SVD and
PCA in medical image tilt correction.
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| Cite this article: |
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Meisen Pan,Fen Zhang. Research on Equivalence of SVD and PCA in Medical Image Tilt Correction[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(3): 453-460.
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2015, Vol. 8 
