Share Email Print

Proceedings Paper

MR imaging statistics and its application in image modeling
Author(s): Yue Joseph Wang; Tianhu Lei; Wilfred Sewchand; Seong Ki Mun
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents a new framework on a complete statistical description of MR imaging and its application in image modeling. Particular studies include object variability and thermal noise, statistical properties of pixel images, and stochastic regularities of context images. Six stochastic properties (Gaussianity, stationarity, dependence, ergodicity, Markovian property, inhomogeneity) are justified to form the basis for establishing the stochastic image models. The application of these properties to both pixel image modeling (standard finite normal mixture) and context image modeling (Markov random field) is discussed mathematically. The correct use of the statistical models in image analysis is verified in terms of new observations, theorems, and interpretations.

Paper Details

Date Published: 11 April 1996
PDF: 12 pages
Proc. SPIE 2708, Medical Imaging 1996: Physics of Medical Imaging, (11 April 1996); doi: 10.1117/12.237834
Show Author Affiliations
Yue Joseph Wang, Georgetown Univ. Medical Ctr. (United States)
Tianhu Lei, Univ. of Maryland/Baltimore (United States)
Wilfred Sewchand, Univ. of Maryland/Baltimore (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 2708:
Medical Imaging 1996: Physics of Medical Imaging
Richard L. Van Metter; Jacob Beutel, Editor(s)

© SPIE. Terms of Use
Back to Top