Citation

Ali, Nur Hasanah and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Muda, Ahmad Sobri and Sutikno, Tole and Jopri, Mohd Hatta (2021) Brain stroke computed tomography images analysis using image processing: A Review. IAES International Journal of Artificial Intelligence (IJ-AI), 10 (4). p. 1048. ISSN 2089-4872

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Abstract

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.

Item Type:	Article
Uncontrolled Keywords:	Image processing, Brain stroke, Computed tomography, CT scan, Medical imaging, Segmentation
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	11 Jan 2022 08:16
Last Modified:	11 Jan 2022 08:16
URII:	http://shdl.mmu.edu.my/id/eprint/9837

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