Citation

Lim, Sin Ting and Abdul Manap, Nurulfajar (2018) Automated ROI-based compression on brain images using principal component analysis. Journal of Engineering and Applied Sciences, 13 (14). pp. 5961-5970. ISSN 1816-949X

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Abstract

Medical image contains diagnostically important regions that shall not be subjected to lossy compression. In order to increase compression rate for higher transmission and storage capability, a partial compression scheme based on ROI and non-ROI was employed. A manual segmentation technique to separate ROI and non-ROI for thousands of images are impractical, hence in this study an automated brain segmentation technique was developed to work with a PCA compression scheme. Non-ROI region will be compressed by PCA compression while ROI region will be preserved. The segmentation technique specifically tailored for brain segmentation has successfully separate ROI and non-ROI regions and results indicate that image quality is higher for image undergo the proposed model compared with image without ROI segmentation.

Item Type:	Article
Uncontrolled Keywords:	Diagnostic imaging, medical image compression
Subjects:	R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Nov 2020 18:26
Last Modified:	09 Nov 2020 18:26
URII:	http://shdl.mmu.edu.my/id/eprint/7282

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