Research on Equivalence of SVD and PCA in Medical Image Tilt Correction (Journal of Fiber Bioengineering and Informatics, 2015)
In the process of medical imaging, often because of some disturbance, the medical images frequentlyhave 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 ComponentAnalysis (PCA) are studied and their relationship between them is discussed, and then the medicalimage correction tilt process is divided into five main stages. Among these stages, the key tasks focus onfinding the centroid and obtaining the tilt angle of a medical image. We use SVD and PCA to computethe eigenvectors of the coordinates of a medical image respectively to get the tilt angle. The experimentalresults reveal that the methods mentioned above are effective for correcting the tilt medical images andalso prove the equivalence of SVD and PCA in medical image tilt correction.