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Algorithms 2009, 2(2), 850-878; doi:10.3390/a2020850

Bayesian Maximum Entropy Based Algorithm for Digital X-ray Mammogram Processing

University of Bucharest, PO Box MG-11, Bucharest, Romania
Received: 5 December 2008 / Revised: 11 May 2009 / Accepted: 28 May 2009 / Published: 9 June 2009
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Download PDF [1016 KB, uploaded 9 June 2009]

Abstract

Basics of Bayesian statistics in inverse problems using the maximum entropy principle are summarized in connection with the restoration of positive, additive images from various types of data like X-ray digital mammograms. An efficient iterative algorithm for image restoration from large data sets based on the conjugate gradient method and Lagrange multipliers in nonlinear optimization of a specific potential function was developed. The point spread function of the imaging system was determined by numerical simulations of inhomogeneous breast-like tissue with microcalcification inclusions of various opacities. The processed digital and digitized mammograms resulted superior in comparison with their raw counterparts in terms of contrast, resolution, noise, and visibility of details.
Keywords: Bayesian inference; inverse problems; digital image restoration; X-ray mammography; maximum entropy methods Bayesian inference; inverse problems; digital image restoration; X-ray mammography; maximum entropy methods
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Mutihac, R. Bayesian Maximum Entropy Based Algorithm for Digital X-ray Mammogram Processing. Algorithms 2009, 2, 850-878.

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