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Proceedings Paper

Recognition of clustered microcalcifications using a random field model
Author(s): Nico Karssemeijer
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Paper Abstract

A parallel algorithm has been developed to detect clustered microcalcifications in digital mammography. Labeling of the image is performed by a deterministic relaxation scheme in which both image data and prior beliefs are weighted simultaneously using a Bayesian scheme. The image data is represented by parameter images representing local contrast and shape. A random field models contextual relations between pixel labels, which enables bringing in prior knowledge about the spatial properties of the structures to be detected. By defining long range interaction between background and calcification labels the detector can be tuned to be more sensitive inside clusters than outside, ensuring that isolated spots will only be interpreted as calcifications if they are in the neighborhood of others. In this paper attention is focused on the random field model. Different choices of the energy function defining the interaction model are investigated experimentally using a set of 40 mammograms digitized at 2 k2.

Paper Details

Date Published: 29 July 1993
PDF: 11 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148689
Show Author Affiliations
Nico Karssemeijer, Univ. Hospital Nijmegen (Netherlands)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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