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Proceedings Paper

Noisy fractional Brownian motion for detection of perturbations in regular textures
Author(s): Herve Guillemet; Habib Benali; Francoise J. Preteux; Robert Di Paola
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Paper Abstract

A generic method for detecting the presence of perturbating signal in model-based textures is presented. An index quantifying the accuracy of the texture model is defined from estimates of maximum likelihood or maximum a posteriori. The index is computed locally and a threshold value is used to detect those parts of the texture that depart from the model. We investigate the particular case of fractal textures based on a noisy fractional Brownian motion model. A specific accuracy index is derived from the likelihood of a heuristic synthesis model known as the random midpoint displacement algorithm. The method is applied to the problem of detecting microcalcifications in digital mammography. Results show that 95 percent of the breast tissue can be classified as not containing microcalcifications, in a short computation time and without significant error, thus proving the relevance of the method.

Paper Details

Date Published: 8 October 1996
PDF: 12 pages
Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); doi: 10.1117/12.253452
Show Author Affiliations
Herve Guillemet, INSERM (France)
Habib Benali, INSERM (France)
Francoise J. Preteux, Institut National des Telecommunications (France)
Robert Di Paola, INSERM (France)


Published in SPIE Proceedings Vol. 2823:
Statistical and Stochastic Methods for Image Processing
Edward R. Dougherty; Francoise J. Preteux; Jennifer L. Davidson, Editor(s)

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