Browsing by Subject "Calcification (biochemistry)"
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Item Open Access Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition(IEEE, 1997) Ansari, R.; Gürcan, M. Nafi; Yardımcı, Yasemin; Çetin, A. EnisIn this paper, computer-aided detection and enhancement of microcalcifications in mammogram images are considered. The mammogram image is first decomposed into subimages using a `subband' decomposition filter bank which uses nonlinear filters. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. All regions with high positive skewness and kurtosis are marked as a regions of interest. Next, an outlier labeling method is used to find the locations of microcalcifications in these regions. An enhanced mammogram image is also obtained by emphasizing the microcalcification locations. Linear and nonlinear subband decomposition structures are compared in terms of their effectiveness in finding microcalcificated regions and their computational complexity. Simulation studies based on real mammogram images are presented.Item Open Access Detection of microcalcifications in mammograms using nonlinear subband decomposition and outlier labeling(SPIE, 1997-02) Gürcan, M. Nafi; Yardımcı, Yasemin C.; Çetin, A. Enis; Ansari, R.Computer-aided diagnosis will be an important feature of the next generation picture archiving and communication systems. In this paper, computer-aided detection of microcalcifications in mammograms using a nonlinear subband decomposition and outlier labeling is examined. The mammogram image is first decomposed into subimages using a nonlinear subband decomposition filter bank. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. A region with high positive skewness and kurtosis is marked as a region of interest. Finally, an outlier labeling method is used to find the locations of microcalcifications in these regions. Simulation studies are presented.Item Open Access Influence function based Gaussianity tests for detection of microcalcifications in mammogram images(IEEE, 1999-10) Gürcan, M. Nafi; Yardımcı, Y.; Çetin, A. EnisIn 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.