A methodology for modeling the distributions of medical images and their stochastic properties

The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 1990-12, Vol.9 (4), p.376-383
Main Authors: Zhang, Y.-Q., Loew, M.H., Pickholtz, R.L.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biomedical imaging
Data compression
Entropy
Gaussian distribution
Histograms
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Physics
Probability density function
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Radiography
Stochastic processes
Testing
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Summary:The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model (GGAR) is proposed. Its properties and limitations are also discussed. It is expected that the GGAR model will be useful in describing the stochastic characteristics of some classes of medical images and in image data compression and other applications.< >
ISSN:0278-0062
1558-254X
DOI:10.1109/42.61753