Influence function based Gaussianity tests for detection of microcalcifications in mammogram images
Author
Gürcan, M. Nafi
Yardımcı, Y.
Çetin, A. Enis
Date
1999-10Source Title
Proceedings 1999 International Conference on Image Processing
Publisher
IEEE
Pages
407 - 411
Language
English
Type
Conference PaperItem Usage Stats
125
views
views
50
downloads
downloads
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.
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