Medical Image Archiving System Implementation with Lossless Region of Interest and Optical Character Recognition

Basit Öğe Kaydı
dc.contributor.authorErguzen, Atilla
dc.contributor.authorErdal, Erdal
dc.date.accessioned2020-06-25T18:22:41Z
dc.date.available2020-06-25T18:22:41Z
dc.date.issued2017
dc.departmentKırıkkale Üniversitesi
dc.descriptionERDAL, Erdal/0000-0003-1174-1974
dc.description.abstractDigital medical images have been widely used in all stages of healthcare. It has a vital role to transfer and to store digital medical images for medical experts and patients. Since the large file sizes and storage space requirements, image compression has become a necessity. Instead of compressing the entire image, it is an option to compress the region of interest (ROI). Applying lossless methods to the whole image does not provide a sufficient advantage, however, when lossy techniques are used, the vital information of the medical image may be lost. In this study, a novel medical image archiving system implementation based on ROI and Optical Character Recognition (OCR) is proposed. Besides, a new dynamic file structure was used that was specially designed to produce better compression ratio and performance. The medical image is separated into the ROI and the non-ROI parts. JPEG-LS, a lossless compression algorithm, is applied to the ROI segment of the medical image. The OCR and Huffman coding algorithm is used for the non-ROI part of the image. The proposed method was evaluated using medical images of the actual patient and the produced compression ratio for the non-ROI image is between 92.12% and 97.84%. The average difference between the proposed method and the state-of-art in the literature is 83.80% for the non-ROI part. In conclusion, the proposed method provides an integrated solution to the medical image archiving problem.en_US
dc.description.sponsorshipKirikkale University Department of Scientific Research Projects [2016/110]en_US
dc.description.sponsorshipThis work is partly supported by the Kirikkale University Department of Scientific Research Projects (2016/110). The authors would like to thanks, Dr. Erdem Kamil Yildirim, Dr. Ertu. grulam and Dr. Murat Luy for proofreading and comments of the manuscript. Also, thank Huseyin Aydilek from Kirikkale University for providing in part of Matlab implementation of the algorithm.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1166/jmihi.2017.2156
dc.identifier.endpage1252en_US
dc.identifier.issn2156-7018
dc.identifier.issn2156-7026
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85030632848
dc.identifier.scopusqualityN/A
dc.identifier.startpage1246en_US
dc.identifier.urihttps://doi.org/10.1166/jmihi.2017.2156
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6861
dc.identifier.volume7en_US
dc.identifier.wosWOS:000412167300018
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAmer Scientific Publishersen_US
dc.relation.ispartofJournal Of Medical Imaging And Health Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMedical Imageen_US
dc.subjectRegion of Interesten_US
dc.subjectImage Compressionen_US
dc.subjectOptical Character Recognitionen_US
dc.titleMedical Image Archiving System Implementation with Lossless Region of Interest and Optical Character Recognitionen_US
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu