Principal component analysis (PCA) is a mathematical procedure which uses sophisticated mathematical principles to transform a number of correlated variables into a smaller number of variables called principal components. In PCA, the information contained in a set of data is stored with reduced dimensions based on the integral projection of the dataset onto a subspace generated by a system of orthogonal axes. The reduced dimensions computational content is selected so that the significant data characteristics are identified with little information loss. Such a reduction is an advantage in several fields as for image compression, data representation, etc. It can also be widely used for feature extraction, image fusion, image compression, image segmentation, image registration, de-noising, etc. This paper presents a survey of the applications of PCA in the field of medical image processing. In this study, various medical image application-based PCA results are exhibited to prove its efficiency.