Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6466
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dc.contributor.authorArtan, Yusuf-
dc.contributor.authorOto, Aytekin-
dc.contributor.authorYetik, İmam Şamil-
dc.date.accessioned2021-09-11T15:36:42Z-
dc.date.available2021-09-11T15:36:42Z-
dc.date.issued2013en_US
dc.identifier.issn1057-7149-
dc.identifier.issn1941-0042-
dc.identifier.urihttps://doi.org/10.1109/TIP.2013.2285626-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6466-
dc.description.abstractProstate cancer localization using supervised classification techniques has aroused considerable interest in medical imaging community in recent years. However, it is crucial to have an accurate training data set for supervised classification techniques. Since different devices with, e.g., different protocols and/or field strengths cause different intensity profiles, each device/protocol must have an accompanying training data set, which is very costly to obtain. It is highly desirable to adapt the existing classifier(s) trained for one device/protocol to help classify data coming from another device/protocol. In this paper, we propose a novel method that has the ability to design classifiers obtained from one imaging protocol and/or MRI device to be used on a data set from another protocol and/or imaging device. As an example problem, we consider prostate cancer localization with multiparametric MRI. We show that simple normalization techniques such as z-score are not sufficient for cross-device automated cancer localization. On the other hand, the method we have originally developed based on relative intensity allows us to successfully use a classifier obtained from one device to be applied on a test patient imaged with another device. Proposed method also allows us to employ T2-weighted MR images directly instead of an additional step to normalize T2-weighted images usually performed in an ad hoc manner when T2 maps are not available. To demonstrate the effectiveness of the proposed method, we use a multiparametric MRI data set acquired from 18 biopsy-confirmed cancer patients with two separate scanners: 1) 1.5-T (Excite HD) GE and 2) 1.5-T (Achieva) Philips Healthcare scanners. A comprehensive visual, quantitative, and statistical analysis of the results show that methods we have developed allow us to: 1) perform cross-device automated classification and 2) use T2-weighted images without an ad hoc subject-specific normalization.en_US
dc.description.sponsorshipEU Marie Curie COFUND/TUBITAK Cocirc 2236 [112C011]en_US
dc.description.sponsorshipThis work was supported by EU Marie Curie COFUND/TUBITAK Cocirc 2236 under Grant 112C011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Olivier Bernard.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Image Processingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIntensity normalizationen_US
dc.subjectmagnetic resonance imaging (MRI)en_US
dc.subjectprostate canceren_US
dc.subjectdiscriminant analysisen_US
dc.titleCross-Device Automated Prostate Cancer Localization With Multiparametric Mrien_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume22en_US
dc.identifier.issue12en_US
dc.identifier.startpage5385en_US
dc.identifier.endpage5394en_US
dc.authorid0000-0002-7330-4692-
dc.identifier.wosWOS:000331203200018en_US
dc.institutionauthorYetik, Imam Şamil-
dc.identifier.pmid24236301en_US
dc.identifier.doi10.1109/TIP.2013.2285626-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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