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

Zhai, Xiaojun and Eslami, Mohammad and Hussein, Ealaf Sayed and Filali, Maroua Salem and Shalaby, Salma Tarek and Amira, Abbes and Bensaali, Faycal and Dakua, Sarada and Abinahed, Julien and Al-Ansari, Abdulla and Ahmed, Ayman Z (2018) 'Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip.' Journal of Computational Science, 27. pp. 35-45. ISSN 1877-7503

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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.

Item Type: Article
Uncontrolled Keywords: Cerebral aneurysm; Image segmentation; Zynq SoC; FPGA
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Sep 2018 08:58
Last Modified: 15 Jan 2022 01:25

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