Show simple item record

dc.contributor.authorGöçeri, Evgin
dc.contributor.authorÜnlü, Mehmet Zübeyir
dc.contributor.authorGüzeliş, Cüneyt
dc.contributor.authorDicle, Oğuz
dc.date.accessioned2017-03-22T07:50:49Z
dc.date.available2017-03-22T07:50:49Z
dc.date.issued2012
dc.identifier.citationGöçeri, E., Ünlü, M. Z., Güzeliş, C., and Dicle, O. (2012, October). An automatic level set based liver segmentation from MRI data sets. Paper presented at the 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012. doi:10.1109/IPTA.2012.6469551en_US
dc.identifier.isbn9781467325837
dc.identifier.urihttp://doi.org/10.1109/IPTA.2012.6469551
dc.identifier.urihttp://hdl.handle.net/11147/5120
dc.description3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012; Istanbul; Turkey; 15 October 2012 through 18 October 2012en_US
dc.description.abstractA fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results. © 2012 IEEE.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/IPTA.2012.6469551en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGeometric active contoursen_US
dc.subjectLevel set methoden_US
dc.subjectLiver segmentationen_US
dc.subjectMRIen_US
dc.subjectMagnetic resonance imagingen_US
dc.titleAn automatic level set based liver segmentation from MRI data setsen_US
dc.typeconferenceObjecten_US
dc.contributor.authorIDTR42462en_US
dc.contributor.iztechauthorÜnlü, Mehmet Zübeyir
dc.relation.journal3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012en_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.startpage192en_US
dc.identifier.endpage197en_US
dc.identifier.wosWOS:000317076900034
dc.identifier.scopusSCOPUS:2-s2.0-84875853652
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record