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.


CONCLUSIONS
The quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.
Instituto do Cérebro, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil.
Rafael do Espírito
Rdo E
Santo
D001921:Brain; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D025341:Principal Component Analysis / Q000379:methods*