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.
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| Format: | Digital revista |
| Language: | English |
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Instituto Israelita de Ensino e Pesquisa Albert Einstein
2012
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082012000200004 |
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