Increasing Lesion Specificity with Fusion of Manually and Automatically Segmented Liver MR Images
| dc.contributor.author | Ervural, Saim | |
| dc.contributor.author | Ceylan, Murat | |
| dc.date.accessioned | 2020-03-26T19:54:21Z | |
| dc.date.available | 2020-03-26T19:54:21Z | |
| dc.date.issued | 2018 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description | 26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY | en_US |
| dc.description.abstract | In this study, it is aimed to analyze the magnetic resonance (MR) images used in the diagnosis of liver focal lesions using image fusion methods and to help diagnosis by adding automatic segmentation results to the manual segmentation process preferred by experts. For this aim fusions of liver MR images, segmented by a fuzzy method and segmented manually. 120 T1-weighted dynamic contrast-enhanced liver MR images of pre-contrast phase, arterial phase, portal vein phase and late venous phase, taken from 30 different patients, were used. Each phase image is also fused with images segmented by the fuzzy c-means algorithm in the same phase, so that the lesion surfaces and contours are displayed on the segmented image manually. Thus, the significance of the lesion was increased before the information in the MR image in which the liver function information was displayed was lost. The resulting new image contains more useful information for automatic decision systems. The results obtained were evaluated using structural similarity index, peak signal-to-noise ratio and fusion factor quality metrics. | en_US |
| dc.description.sponsorship | IEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univ | en_US |
| dc.identifier.isbn | 978-1-5386-1501-0 | |
| dc.identifier.issn | 2165-0608 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/36712 | |
| dc.identifier.wos | WOS:000511448500412 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | en_US |
| dc.relation.ispartofseries | Signal Processing and Communications Applications Conference | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | discrete wavelet transform | en_US |
| dc.subject | fuzzy c-means | en_US |
| dc.subject | segmentation | en_US |
| dc.subject | image fusion | en_US |
| dc.title | Increasing Lesion Specificity with Fusion of Manually and Automatically Segmented Liver MR Images | en_US |
| dc.type | Conference Object | en_US |
