Principal Component Analysis applied to digital image compression

Objective: To describe the use of a statistical tool (Principal ComponentAnalysis – 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 madeby means of the explanation of eigenvalues and eigenvectors of amatrix. This concept is presented on a digital image collected in theclinical routine of a hospital, based on the functional aspects of amatrix. The analysis of potential for recovery of the original imagewas made in terms of the rate of compression obtained. Results: Thecompressed medical images maintain the principal characteristicsuntil approximately one-fourth of their original size, highlighting theuse of Principal Component Analysis as a tool for image compression.Secondarily, the parameter obtained may reflect the complexityand potentially, the texture of the original image. Conclusion: Thequantity of principal components used in the compression influencesthe recovery of the original image from the final (compacted) image.