Influence function based Gaussianity tests for detection of microcalcifications in mammogram images

Date
1999-10
Advisor
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Source Title
Proceedings 1999 International Conference on Image Processing
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
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Pages
407 - 411
Language
English
Type
Conference Paper
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Abstract

In this paper, computer-aided diagnosis of microcalcifications in mammogram images is considered. Microcalcification clusters are an early sign of breast cancer. Microcalcifications appear as single bright spots in mammogram images. We propose an effective method for the detection of these abnormalities. The first step of this method is two-dimensional adaptive filtering. The filtering produces an error image which is divided into overlapping square regions. In each square region, a Gaussianity test is applied. Since microcalcifications have an impulsive appearance, they are treated as outliers. In regions with no microcalcifications, the distribution of the error image is almost Gaussian, on the other hand, in regions containing microcalcification clusters, the distribution deviates from Gaussianity. Using the theory of the influence function and sensitivity curves, we develop a Gaussianity test. Microcalcification clusters are detected using the Gaussianity test. Computer simulation studies are presented.

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Keywords
Calcification (biochemistry), Computer aided analysis, Computer aided diagnosis, Computer simulation, Error analysis, Functions, Image analysis, Oncology, Sensitivity analysis, Signal filtering and prediction, Gaussianity tests, Microcalcification, Mammography
Citation
Published Version (Please cite this version)