2-D adaptive prediction based Gaussianity tests in microcalcification detection

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage633en_US
dc.citation.spage625en_US
dc.contributor.authorGürcan, M. Nafien_US
dc.contributor.authorYardımcı, Yaseminen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialSan Jose, CA, United States
dc.date.accessioned2016-02-08T11:59:03Z
dc.date.available2016-02-08T11:59:03Z
dc.date.issued1998-01en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 24-30 January, 1998
dc.descriptionConference name: Photonics West '98 Electronic Imaging - Visual Communications and Image Processing '98
dc.description.abstractWith 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.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T11:59:03Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 1998en
dc.identifier.doi10.1117/12.298376en_US
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11693/27664
dc.language.isoEnglishen_US
dc.publisherSPIE
dc.relation.isversionofhttps://doi.org/10.1117/12.298376en_US
dc.source.titleProceedings - Visual Communications and Image Processing '98 - Photonics West '98 Electronic Imagingen_US
dc.subjectAdaptive filteringen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer aided diagnosisen_US
dc.subjectDatabase systemsen_US
dc.subjectMammographyen_US
dc.subjectGaussianity testsen_US
dc.subjectMammogramsen_US
dc.subjectImage processingen_US
dc.title2-D adaptive prediction based Gaussianity tests in microcalcification detectionen_US
dc.typeConference Paperen_US

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