Performance analysis of a new semiorthogonal spline wavelet compression algorithm for tonal medical images

Lossy image compression is thought to be a necessity as radiology moves toward a filmless environment. Compression algorithms based on the discrete cosine transform (DCT) are limited due to the infinite support of the cosine basis function. Wavelets, basis functions that have compact or nearly compa...

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Bibliographic Details
Published in:Medical physics (Lancaster) 2000-02, Vol.27 (2), p.276-288
Main Authors: Thompson, Stephen K., Hazle, John D., Schomer, Donald F., Elekes, Almos A., Johnston, Dennis A., Huffman, John, Chui, Charles K.
Format: Article
Language:English
Subjects:
Algorithms
Angiography
biomedical MRI
biomedical ultrasonics
Computed radiography
Computed tomography
computerised tomography
data compression
Diagnostic Imaging
diagnostic radiography
discrete cosine transforms
Fluoroscopy
Humans
Image analysis
Image Processing, Computer-Assisted
Magnetic resonance
Magnetic Resonance Imaging
Medical diagnosis with acoustics
medical image processing
Medical image reconstruction
Medical imaging
Medical X‐ray imaging
Models, Theoretical
Nuclear Medicine
Photons
Radiography
single photon emission computed tomography
Single photon emission computed tomography (SPECT)
splines (mathematics)
Tomography, Emission-Computed, Single-Photon
Tomography, X-Ray Computed
Ultrasonography
wavelet transforms
Wavelets
X‐ray imaging
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Summary:Lossy image compression is thought to be a necessity as radiology moves toward a filmless environment. Compression algorithms based on the discrete cosine transform (DCT) are limited due to the infinite support of the cosine basis function. Wavelets, basis functions that have compact or nearly compact support, are mathematically better suited for decorrelating medical image data. A lossy compression algorithm based on semiorthogonal cubic spline wavelets has been implemented and tested on six different image modalities (magnetic resonance, x-ray computed tomography, single photon emission tomography, digital fluoroscopy, computed radiography, and ultrasound). The fidelity of the reconstructed wavelet images was compared to images compressed with a DCT algorithm for compression ratios of up to 40:1. The wavelet algorithm was found to have generally lower average error metrics and higher peak-signal-to-noise ratios than the DCT algorithm.
ISSN:0094-2405
2473-4209
DOI:10.1118/1.598830