Medical image compression based on a morphological representation of wavelet coefficients

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 nove...

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Bibliographic Details
Published in:Medical physics (Lancaster) 1999-08, Vol.26 (8), p.1607-1611
Main Authors: Phelan, Niall C., Ennis, Joseph T.
Format: Article
Language:English
Subjects:
Algorithms
Biophysical Phenomena
Biophysics
Brain - diagnostic imaging
data compression
General theory and mathematical aspects
Humans
image compression
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - statistics & numerical data
image quality
image storage
image transmission
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - statistics & numerical data
Medical diagnosis
medical image processing
Medical image quality
Medical image reconstruction
medical imaging
PACS
Physicists
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Spine - anatomy & histology
Tomography, X-Ray Computed - methods
Tomography, X-Ray Computed - statistics & numerical data
wavelet transform
wavelet transforms
Wavelets
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Summary: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.
ISSN:0094-2405
2473-4209
DOI:10.1118/1.598655