Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
Sin Ting Lim and Nurulfajar Bin Abdul Manap. Automated ROI-Based Compression on Brain Images Using
Principal Component Analysis.
DOI: https://doi.org/10.36478/jeasci.2018.5961.5970
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.5961.5970