@article{MAKHILLJEAS2018131416599,
    title = {Automated ROI-Based Compression on Brain Images Using
Principal Component Analysis},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {14},
    pages = {5961-5970},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.5961.5970},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.5961.5970},
    author = {Nurulfajar and},
    keywords = {Medical image compression,ROI,automated segmentation,compression,important,PCA},
    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.}
    }