Show simple item record

dc.contributor.authorZhai, Xiaojun
dc.contributor.authorEslami, Mohammad
dc.contributor.authorHussein, Ealaf Sayed
dc.contributor.authorFilali, Maroua Salem
dc.contributor.authorShalaby, Salma Tarek
dc.contributor.authorAmira, Abbes
dc.contributor.authorBensaali, Faycal
dc.contributor.authorDakua, Sarada
dc.contributor.authorAbinahed, Julien
dc.contributor.authorAl-Ansari, Abdulla
dc.contributor.authorAhmed, Ayman Z.
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.
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
html.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.


Files in this item

Thumbnail
Name:
Publisher version
Thumbnail
Name:
Zhai_2018_Real-time_automated_ ...
Size:
1.257Mb
Format:
PDF
Description:
Author Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record