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Proceedings Paper

Multiresolution texture analysis of bone radiographs using Gaussian Markov random-field models
Author(s): Jagath K. Samarabandu; Raj S. Acharya; E. Hausmann; K. A. Allen
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Paper Abstract

Texture analysis of bone radiographs can play an important role in characterizing the progression of bone diseases by computing texture measures on the digitized bone radiographs. Fractal dimension is one such texture measure which has been used with success as radiographs of trabecular bone are shown to exhibit self-similar characteristics. Markov random fields (MRF) have been used successfully to classify texture by modeling it as stochastic processes. But it has been shown that MRF models do not perform well in modeling self-similar textures such as fractional Brownian motion (FBM). This limitation can be overcome by characterizing statistical properties of the incremental process which builds up a fractal object. Since we try to characterize the statistical properties of the incremental process which builds the fractal object rather than its multi scale behavior using Gaussian MRF, this approach is complimentary to using fractal dimension as a feature in characterizing texture.

Paper Details

Date Published: 1 May 1994
PDF: 7 pages
Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994); doi: 10.1117/12.174417
Show Author Affiliations
Jagath K. Samarabandu, SUNY/Buffalo (United States)
Raj S. Acharya, SUNY/Buffalo (United States)
E. Hausmann, SUNY/Buffalo (United States)
K. A. Allen, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 2168:
Medical Imaging 1994: Physiology and Function from Multidimensional Images
Eric A. Hoffman; Raj S. Acharya, Editor(s)

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