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Vol.10, No.3, August 2021. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Adaptive Image Segmentation for Traumatic Brain Haemorrhage
Ahmad Yahya Dawod, Aniwat Phaphuangwittayakul
© 2021 Ahmad Yahya Dawod, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 10, Issue 3, Pages 1476-1487, ISSN 2217-8309, DOI: 10.18421/TEM103-61, August 2021.
Received: 28 May 2021. Revised: 10 August 2021.
Abstract:
It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.
Keywords : Traumatic Brain Injury, segmentation, DRLSE, Thresholding, Morphological. |
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