A two-view ultrasound CAD system for spina bifida detection using Zernike features

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dc.authoridKonur, Umut/0000-0003-1322-6669
dc.authorwosidGurgen, Fikret/AAD-6623-2020
dc.authorwosidKonur, Umut/A-1835-2019
dc.contributor.authorKonur, Umut
dc.contributor.authorGurgen, Fikret
dc.contributor.authorVarol, Fusun
dc.date.accessioned2024-06-12T11:15:29Z
dc.date.available2024-06-12T11:15:29Z
dc.date.issued2011
dc.departmentTrakya Üniversitesien_US
dc.descriptionConference on Medical Imaging 2011 - Computer-Aided Diagnosis -- FEB 15-17, 2011 -- Lake Buena Vista, FLen_US
dc.description.abstractIn this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both modalities are available, those decisions can be combined to keep matters more secure. Even more security can be attained by using more than two modalities and base the final decision on all those potential classifiers. Our current system relies on feature extraction from images for cases (for particular patients). The first step is image preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain, which is hopefully more representative for good classification performance. Next, a particular type of feature extraction, which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image modality being used. Either shape or texture information captured by moments may propose useful features. Finally, SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.en_US
dc.description.sponsorshipSPIE,Dynasil Corp/RMD Res,Amer Assoc Physicists Med,DQE Instruments, Inc,Ocean Thin Films, Inc,Univ Cent Florida, CREOL - Coll Opt & Photon,VIDA Diagnost, Incen_US
dc.description.sponsorshipLale Akarun of Computer Eng. Dept., Bogazici University; Bogazici University Research Fund [BAP5179]en_US
dc.description.sponsorshipWe would like to thank Lale Akarun of Computer Eng. Dept., Bogazici University for her valuable feedback and support. We also express our thanks to Ibrahim Kalelioglu and Atyl Yuksel of Obstetrics and Gynecology Dept., Faculty of Medicine, Istanbul University for their help that made the realization of this work possible and especially for US image data provision. This work is supported by Bogazici University Research Fund with project number BAP5179.en_US
dc.identifier.doi10.1117/12.878458
dc.identifier.isbn978-0-81948-505-2
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-79955759475en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1117/12.878458
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23958
dc.identifier.volume7963en_US
dc.identifier.wosWOS:000294211100136en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofMedical Imaging 2011: Computer-Aided Diagnosisen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpina Bifidaen_US
dc.subjectComputer Aided Detection/Diagnosisen_US
dc.subjectSegmentationen_US
dc.subjectZernike Momentsen_US
dc.subjectSVMen_US
dc.subjectComputer-Aided Diagnosisen_US
dc.subjectMassesen_US
dc.titleA two-view ultrasound CAD system for spina bifida detection using Zernike featuresen_US
dc.typeConference Objecten_US

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