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Proceedings Paper

Modeling of digital mammograms using bicubic spline functions and additive noise
Author(s): Christine Graffigne; Aboubakar Maintournam; Anne Strauss
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Paper Abstract

The purpose of our work is the microcalcifications detection on digital mammograms. In order to do so, we model the grey levels of digital mammograms by the sum of a surface trend (bicubic spline function) and an additive noise or texture. We also introduce a robust estimation method in order to overcome the bias introduced by the microcalcifications. After the estimation we consider the subtraction image values as noise. If the noise is not correlated, we adjust its distribution probability by the Pearson's system of densities. It allows us to threshold accurately the images of subtraction and therefore to detect the microcalcifications. If the noise is correlated, a unilateral autoregressive process is used and its coefficients are again estimated by the least squares method. We then consider non overlapping windows on the residues image. In each window the texture residue is computed and compared with an a priori threshold. This provides correct localization of the microcalcifications clusters. However this technique is definitely more time consuming that then automatic threshold assuming uncorrelated noise and does not lead to significantly better results. As a conclusion, even if the assumption of uncorrelated noise is not correct, the automatic thresholding based on the Pearson's system performs quite well on most of our images.

Paper Details

Date Published: 24 September 1998
PDF: 9 pages
Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); doi: 10.1117/12.323452
Show Author Affiliations
Christine Graffigne, Univ. de Paris V--Univ. Rene Descartes (France)
Aboubakar Maintournam, Univ. de Paris V--Univ. Rene Descartes (France)
Anne Strauss, Univ. de Paris VI--Univ. Pierre et Marie Curie (France)

Published in SPIE Proceedings Vol. 3457:
Mathematical Modeling and Estimation Techniques in Computer Vision
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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