Principal Component Analysis applied to digital image compression

objective: to describe the use of a statistical tool (principal component analysis – pca) for the recognition of patterns and compression, applying these concepts to digital images used in medicine. methods: the description of principal component analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. this concept is presented on a digital image collected in the clinical routine of a hospital, based on the functional aspects of a matrix. the analysis of potential for recovery of the original image was made in terms of the rate of compression obtained. results: the compressed medical images maintain the principal characteristics until approximately one-fourth of their original size, highlighting the use of principal component analysis as a tool for image compression. secondarily, the parameter obtained may reflect the complexity and potentially, the texture of the original image. conclusion: the quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.