Computer aided detection of spina bifida using nearest neighbor classification with curvature scale space features of fetal skulls extracted from ultrasound images

Basit Öğe Kaydı
dc.authoridKonur, Umut/0000-0003-1322-6669
dc.authoridAkarun, Lale/0000-0002-8813-8084
dc.authorwosidKonur, Umut/A-1835-2019
dc.authorwosidGurgen, Fikret/AAD-6623-2020
dc.authorwosidAkarun, Lale/AAR-7734-2020
dc.contributor.authorKonur, Umut
dc.contributor.authorGurgen, Fikret S.
dc.contributor.authorVarol, Fusun
dc.contributor.authorAkarun, Lale
dc.date.accessioned2024-06-12T10:59:00Z
dc.date.available2024-06-12T10:59:00Z
dc.date.issued2015
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThis paper addresses the problem of detecting the common neural tube defect of spina bifida by a computer aided detection (CAD) system. We propose a Method which extracts the curvature scale space (CSS) features of fetal skull contours viewed in the ultrasound (US) modality and performs nearest neighbor (kNN) classification on those features having the desired properties of invariance with respect to translation, orientation and scale changes, thus improving robustness. The distance between two sets of CSS features, each set corresponding to the description of the contour of a particular skull, is measured as the cost of matching the two sets of CSS features. Such a CAD system may act as a second observer and help experts in prenatal diagnosis. Our data possess absolute and relative rarity. The experiments are performed with two different rare class handling methods and over a range of operating conditions. All experiments are based on a group of settings; associated with using either balanced or unbalanced datasets, employing different types of CSS features and how CSS matching costs are computed. Comparatively evaluating the classification performance of the settings is carried with the aid of the whole-curve metric of area under the receiver operating characteristics (ROC) curve (AUC). Optimal operating conditions for any setting can be identified and some settings reveal advantages over others. The observations indicate that using balanced datasets offers better performance and our proposed version of estimating CSS matching costs is generally superior to the classical method. Furthermore, using enhanced sets of CSS features improves classification accuracy. When classification is performed on balanced data using enhanced CSS features and the matching cost is computed with our proposed technique; one can observe an F-measure of 0.76 along with 70% TP rate (recall), 17% FP rate (false alarms) and 82% precision. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipBogazici University [BAP 14A01P2]; Turkish Ministry of Development [2007K120610]en_US
dc.description.sponsorshipWe would like to express our sincere thanks to the Obstetrics and Gynecology Departments of Medical Faculties of Trakya University and Istanbul University. We are grateful to Ibrahim Kalelioglu of Istanbul University for his help in ultrasound image collection. This work is also being supported by the Scientific Research Projects fund (BAP 14A01P2) of Bogazici University and the Turkish Ministry of Development under the TAM Project, Number 2007K120610.en_US
dc.identifier.doi10.1016/j.knosys.2015.04.021
dc.identifier.endpage95en_US
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-84937523376en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage80en_US
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2015.04.021
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20281
dc.identifier.volume85en_US
dc.identifier.wosWOS:000359331000007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCurvature Scale Spaceen_US
dc.subjectDifferential Turning Angleen_US
dc.subjectSpina Bifidaen_US
dc.subjectUltrasounden_US
dc.subjectReceiver Operating Characteristicsen_US
dc.subjectArea Under The ROC Curveen_US
dc.subjectShape Representationen_US
dc.subjectMultiscaleen_US
dc.subjectDiagnosisen_US
dc.subjectCanceren_US
dc.titleComputer aided detection of spina bifida using nearest neighbor classification with curvature scale space features of fetal skulls extracted from ultrasound imagesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu