2-D adaptive prediction based Gaussianity tests in microcalcification detection

Date
1998-01
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Source Title
Proceedings - Visual Communications and Image Processing '98 - Photonics West '98 Electronic Imaging
Print ISSN
0277-786X
Electronic ISSN
Publisher
SPIE
Volume
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Pages
625 - 633
Language
English
Type
Conference Paper
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Abstract

With increasing use of Picture Archiving and Communication Systems (PACS), Computer-aided Diagnosis (CAD) methods will be more widely utilized. In this paper, we develop a CAD method for the detection of microcalcification clusters in mammograms, which are an early sign of breast cancer. The method we propose makes use of two-dimensional (2-D) adaptive filtering and a Gaussianity test recently developed by Ojeda et al. for causal invertible time series. The first step of this test is adaptive linear prediction. It is assumed that the prediction error sequence has a Gaussian distribution as the mammogram images do not contain sharp edges. Since microcalcifications appear as isolated bright spots, the prediction error sequence contains large outliers around microcalcification locations. The second step of the algorithm is the computation of a test statistic from the prediction error values to determine whether the samples are from a Gaussian distribution. The Gaussianity test is applied over small, overlapping square regions. The regions, in which the Gaussianity test fails, are marked as suspicious regions. Experimental results obtained from a mammogram database are presented.

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Keywords
Adaptive filtering, Algorithms, Computer aided diagnosis, Database systems, Mammography, Gaussianity tests, Mammograms, Image processing
Citation
Published Version (Please cite this version)