Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Farklı Kontrastlı Medi̇kal Görüntülerde Elasti̇k Hi̇zalama İ̇çi̇n Ni̇rengi̇ Noktalarının Beli̇rlenmesi̇ ve Sınanması(IEEE, 2011) Karaçalı, BilgeIn this study, automatic identification of landmarks was carried out on real double-echo PD/T2 Magnetic Resonance images for use in multi-modality deformable image registration algorithms. To this end, the corner points of the PD-weighted Magnetic Resonance image selected as the reference image were identified at varying fields of view and used as landmark candidates after eliminating the repeated points and those at close proximity. Next, the matching performances of these candidates were determined by invoking a localization algorithm that optimizes information theoretic point similarity measures derived earlier after random initialization at varying distances and directions. In consideration to the best performing point similarity measures and neighborhood radii at landmarks providing successful localization, the mutual information-based point similarity measure evaluated over large neighborhoods was preferred at a conspicuously higher rate than the other alternatives. © 2011 IEEE.Conference Object Citation - WoS: 42Citation - Scopus: 43An Automatic Level Set Based Liver Segmentation From Mri Data Sets(Institute of Electrical and Electronics Engineers Inc., 2012) Göçeri, Evgin; Ünlü, Mehmet Zübeyir; Güzeliş, Cüneyt; Dicle, OğuzA 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.