CREATING AND COMPARING SETS OF PRINCIPAL COMPONENT IMAGES

Principal component analysis (PCA) of image data sets is a well known technique, and it is used in many disciplines. With present possibilities for interactive explorative work, it is possible to combine preprocessing in scene space or in data space with PCA. Using PCA in this way demands ways of comparing the results. Different ways of using graphics for this purpose is discussed. Another way to compare the results is to perform measurements on the obtained PC image data sets. How this could be done is discussed, and a measure of the local signal-to-noise ratio (SNR) in the PC images is proposed. It is particularly useful since it indicates how well the noise is handled by the PCA, and this is demonstrated using two application examples from medical imaging.