Multiresolution texture analysis of bone radiographs using Gaussian Markov random-field models

Jagath K. Samarabandu; Raj S. Acharya; E. Hausmann; K. A. Allen

doi:10.1117/12.174417

1 May 1994 Multiresolution texture analysis of bone radiographs using Gaussian Markov random-field models

Jagath K. Samarabandu, Raj S. Acharya, E. Hausmann, K. A. Allen

Proceedings Volume 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images; (1994) https://doi.org/10.1117/12.174417
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

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.

Citation Download Citation

Jagath K. Samarabandu, Raj S. Acharya, E. Hausmann, and K. A. Allen "Multiresolution texture analysis of bone radiographs using Gaussian Markov random-field models", Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994); https://doi.org/10.1117/12.174417

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