Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip.

Hdl Handle:
http://hdl.handle.net/10545/622717
Title:
Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip.
Authors:
Zhai, Xiaojun ( 0000-0002-1030-8311 ) ; Eslami, Mohammad; Hussein, Ealaf Sayed; Filali, Maroua Salem; Shalaby, Salma Tarek; Amira, Abbes; Bensaali, Faycal ( 0000-0002-9273-4735 ) ; Dakua, Sarada; Abinahed, Julien; Al-Ansari, Abdulla; Ahmed, Ayman Z.
Abstract:
Cerebral aneurysm is a weakness in a blood vessel that may enlarge and bleed into the surrounding area, which is a life-threatening condition. Therefore, early and accurate diagnosis of aneurysm is highly required to help doctors to decide the right treatment. This work aims to implement a real-time automated segmentation technique for cerebral aneurysm on the Zynq system-on-chip (SoC), and virtualize the results on a 3D plane, utilizing virtual reality (VR) facilities, such as Oculus Rift, to create an interactive environment for training purposes. The segmentation algorithm is designed based on hard thresholding and Haar wavelet transformation. The system is tested on six subjects, for each consists 512 × 512 DICOM slices, of 16 bits 3D rotational angiography. The quantitative and subjective evaluation show that the segmented masks and 3D generated volumes have admitted results. In addition, the hardware implement results show that the proposed implementation is capable to process an image using Zynq SoC in an average time of 5.2 ms.
Affiliation:
University of Derby; Qatar University; Hamad Medical Corporation; Hamad General Hospital
Citation:
Zhai, X. et al (2018) 'Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip' Journal of Computational Science, Vol. 27, pp. 35-45.
Publisher:
Elsevier
Journal:
Journal of Computational Science
Issue Date:
3-May-2018
URI:
http://hdl.handle.net/10545/622717
DOI:
10.1016/j.jocs.2018.05.002
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877750317313005
Type:
Article
Language:
en
ISSN:
18777503
Sponsors:
National Priorities Research Program (NPRP) grant No. 5-792-2-328
Appears in Collections:
Department of Electronics, Computing & Maths

Full metadata record

DC FieldValue Language
dc.contributor.authorZhai, Xiaojunen
dc.contributor.authorEslami, Mohammaden
dc.contributor.authorHussein, Ealaf Sayeden
dc.contributor.authorFilali, Maroua Salemen
dc.contributor.authorShalaby, Salma Tareken
dc.contributor.authorAmira, Abbesen
dc.contributor.authorBensaali, Faycalen
dc.contributor.authorDakua, Saradaen
dc.contributor.authorAbinahed, Julienen
dc.contributor.authorAl-Ansari, Abdullaen
dc.contributor.authorAhmed, Ayman Z.en
dc.date.accessioned2018-05-08T08:48:53Z-
dc.date.available2018-05-08T08:48:53Z-
dc.date.issued2018-05-03-
dc.identifier.citationZhai, X. et al (2018) 'Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip' Journal of Computational Science, Vol. 27, pp. 35-45.en
dc.identifier.issn18777503-
dc.identifier.doi10.1016/j.jocs.2018.05.002-
dc.identifier.urihttp://hdl.handle.net/10545/622717-
dc.description.abstractCerebral aneurysm is a weakness in a blood vessel that may enlarge and bleed into the surrounding area, which is a life-threatening condition. Therefore, early and accurate diagnosis of aneurysm is highly required to help doctors to decide the right treatment. This work aims to implement a real-time automated segmentation technique for cerebral aneurysm on the Zynq system-on-chip (SoC), and virtualize the results on a 3D plane, utilizing virtual reality (VR) facilities, such as Oculus Rift, to create an interactive environment for training purposes. The segmentation algorithm is designed based on hard thresholding and Haar wavelet transformation. The system is tested on six subjects, for each consists 512 × 512 DICOM slices, of 16 bits 3D rotational angiography. The quantitative and subjective evaluation show that the segmented masks and 3D generated volumes have admitted results. In addition, the hardware implement results show that the proposed implementation is capable to process an image using Zynq SoC in an average time of 5.2 ms.en
dc.description.sponsorshipNational Priorities Research Program (NPRP) grant No. 5-792-2-328en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877750317313005en
dc.rightsArchived with thanks to Journal of Computational Scienceen
dc.subjectCerebral aneurysmen
dc.subjectImage segmentationen
dc.subjectZynq system on chipen
dc.subjectField programmable gate array (FPGA)en
dc.subjectVirtual realityen
dc.titleReal-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip.en
dc.typeArticleen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentQatar Universityen
dc.contributor.departmentHamad Medical Corporationen
dc.contributor.departmentHamad General Hospitalen
dc.identifier.journalJournal of Computational Scienceen
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