Paper
12 June 1986 Comparison Of Discrete Cosine Transform And Vector Quantization Of Medical Imagery
Barry G. Haskell, Hsueh-Ming Hang
Author Affiliations +
Proceedings Volume 0626, Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems; (1986) https://doi.org/10.1117/12.975421
Event: Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems (PACS IV) for Medical Applications, 1986, Newport Beach, CA, United States
Abstract
This paper addresses the problem of data compression of medical imagery such as X-rays, Computer Tomography, Magnetic Resonance, Nuclear Medicine and Ultrasound. The Discrete Cosine Transform (DCT) has been extensively studied for image data compression, and good compression has been obtained without unduly sacrificing image quality. Vector Quantization has only recently been applied to image data compression, but shows promise of outperforming more traditional transform coding methods, especially at high compression. Vector Quantization is quite well suited for those applications where the images to be processed are very much alike, or can be grouped into a small number of classifications. These and similar studies continue to suffer from the lack of a uniformly agreed upon measure of image quality. This is also exacerbated by the large variety of electronic displays and viewing conditions.
© (1986) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Barry G. Haskell and Hsueh-Ming Hang "Comparison Of Discrete Cosine Transform And Vector Quantization Of Medical Imagery", Proc. SPIE 0626, Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems, (12 June 1986); https://doi.org/10.1117/12.975421
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CITATIONS
Cited by 5 scholarly publications and 6 patents.
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KEYWORDS
Quantization

Image compression

Medical imaging

Distortion

Computer programming

Spatial frequencies

Image processing

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