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Medical image compression based on a morphological representation of wavelet coefficients
Author(s) -
Phelan Niall C.,
Ennis Joseph T.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598655
Subject(s) - image compression , wavelet , artificial intelligence , data compression , huffman coding , wavelet transform , compression ratio , computer science , computer vision , texture compression , image quality , peak signal to noise ratio , pattern recognition (psychology) , medical imaging , lossless compression , image processing , image (mathematics) , internal combustion engine , automotive engineering , engineering
Image compression is fundamental to the efficient and cost‐effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high‐quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run‐length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal‐to‐noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.