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AuthorZhai, X.
AuthorZhai, Xiaojun
AuthorEslami, Mohammad
AuthorHussein, Ealaf Sayed
AuthorFilali, Maroua Salem
AuthorShalaby, Salma Tarek
AuthorAmira, Abbes
AuthorBensaali, Faycal
AuthorDakua, Sarada
AuthorAbinahed, Julien
AuthorAl-Ansari, Abdulla
AuthorAhmed, Ayman Z.
Available date2019-09-11T10:53:48Z
Publication Date2018-07-01
Publication NameJournal of Computational Science
Identifierhttp://dx.doi.org/10.1016/j.jocs.2018.05.002
CitationZhai, X., Eslami, M., Hussein, E. S., Filali, M. S., Shalaby, S. T., Amira, A., ... & Ahmed, A. Z. (2018). Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip. Journal of computational science, 27, 35-45.‏
ISSN1877-7503
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046644207&origin=inward
URIhttp://hdl.handle.net/10576/11814
Abstract© 2018 Elsevier B.V. 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.
SponsorNPRP,5-792-2-328
Languageen
PublisherElsevier B.V.
SubjectCerebral aneurysm
FPGA
Image segmentation
Zynq SoC
TitleReal-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip
TypeArticle
Pagination35-45
Volume Number27
dc.accessType Abstract Only


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