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dc.contributor.authorGurcan, M.Nafi, Yardimci Yasemin, Cetin, A.Enisen_US
dc.description.abstractIn 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.en_US
dc.source.titleIEEE International Conference on Image Processingen_US
dc.subjectCalcification (biochemistry)en_US
dc.subjectComputer aided analysisen_US
dc.subjectComputer aided diagnosisen_US
dc.subjectComputer simulationen_US
dc.subjectError analysisen_US
dc.subjectImage analysisen_US
dc.subjectSensitivity analysisen_US
dc.subjectSignal filtering and predictionen_US
dc.subjectGaussianity testsen_US
dc.titleInfluence function based Gaussianity tests for detection of microcalcifications in mammogram imagesen_US
dc.typeConference Paperen_US
dc.publisherIEEE, Los Alamitos, CA, United Statesen_US

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