Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)

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dc.contributor.authorCivcik, Levent
dc.contributor.authorYilmaz, Burak
dc.contributor.authorOzbay, Yuksel
dc.contributor.authorEmlik, Ganime Dilek
dc.date.accessioned2020-03-26T19:01:44Z
dc.date.available2020-03-26T19:01:44Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractMicrocalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer (ALIE). In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's research projectsSelcuk University [10101026]en_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's research projects under Project No. 10101026.en_US
dc.identifier.doi10.3906/elk-1303-139en_US
dc.identifier.endpage872en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issn1303-6203en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage853en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1303-139
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31989
dc.identifier.volume23en_US
dc.identifier.wosWOS:000352476800017en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMammogramen_US
dc.subjectmicrocalcificationen_US
dc.subjectcellular neural networksen_US
dc.subjectimage processingen_US
dc.subjectimage enhancementen_US
dc.subjectautomated lesion intensity enhanceren_US
dc.subjectpectoral muscleen_US
dc.titleDetection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)en_US
dc.typeArticleen_US

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