Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2896
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dc.contributor.authorÖzdemir, Galip-
dc.contributor.author Nasıfoğlu, Hüseyin-
dc.contributor.author Eroğul, Osman-
dc.date.accessioned2019-12-25T14:15:48Z
dc.date.available2019-12-25T14:15:48Z
dc.date.issued2019
dc.identifier.citationOzdemir, G., Nasifoglu, H., & Erogul, O. (2019). Performance Comparison of Segmentation Algorithms for Image Quality Degraded MR Images. In World Congress on Medical Physics and Biomedical Engineering 2018 (pp. 243-248). Springer, Singapore.en_US
dc.identifier.isbn9789811090349
dc.identifier.issn1680-0737
dc.identifier.urihttps://doi.org/10.1007/978-981-10-9035-6_44-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2896-
dc.description.abstractMedical image segmentation is one of the most important research areas of clinical diagnosis. Especially, brain is the most critical organ that is tracked, investigated and analyzed mostly by using Magnetic Resonance Imaging (MRI). Developing a highly accurate automated segmentation of brain region is a very difficult task due to involving noise and deviation. In recent years, various image segmentation techniques have been developed in the literature such as clustering, thresholding (intensity-based), active contours (surface-based), expectation maximization (probability-based). In this study, these commonly used algorithms are handled in order to see the performance of the segmentation while degrading the image quality and saving from memory for brain MR images. For this purpose, the level of acceptable degradation is obtained by compressing MR slice images with different quality factors by using JPEG algorithm. Peak signal to noise ratio (PSNR), bits per pixel (BPP), mean, variance parameters of the MR images are used to characterize the corresponding compressed image degradation quality. On the other hand, segmented intracranial area, white matter (WM), gray matter (GM) regions are compared with the non-compressed MR images for various compression ratios. Then, the area overlap ratio for these regions is obtained in order to get segmentation performance results. It is believed that detected optimum parameters can be used as prior indicators to determine which segmentation algorithm (or which group, i.e. intensity or surface-based) should be chosen. Besides, it will be able to occupy less space in memory by compressing image for appropriate parameters.en_US
dc.language.isoenen_US
dc.publisherSpringer Nature Singapore Pte Ltd.en_US
dc.relation.ispartofWorld Congress on Medical Physics and Biomedical Engineering 2018 (WC 2018)en_US
dc.relation.ispartofIFMBE Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSegmentationen_US
dc.subject Compressionen_US
dc.subject Brainen_US
dc.subject MRI en_US
dc.titlePerformance Comparison of Segmentation Algorithms for Image Quality Degraded Mr Imagesen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümütr_TR
dc.identifier.volume68
dc.identifier.issue1
dc.identifier.startpage243
dc.identifier.endpage248
dc.authorid0000-0002-4640-6570-
dc.identifier.wosWOS:000450908300044en_US
dc.identifier.scopus2-s2.0-85048253012en_US
dc.institutionauthorÖzdemir, Galip-
dc.institutionauthorEroğul, Osman-
dc.identifier.doi10.1007/978-981-10-9035-6_44-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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