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Göçeri, Evgin
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03.05. Department of Electrical and Electronics Engineering
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3
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WoS Citation Count
87
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90
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29.00
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30.00
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| Journal | Count |
|---|---|
| 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012 | 1 |
| Turkish Journal of Electrical Engineering and Computer Sciences | 1 |
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Doctoral Thesis A Comparative Evaluation for Liver Segmentation From Spir Images and a Novel Level Set Method Using Signed Pressure Force Function(Izmir Institute of Technology, 2013) Göçeri, Evgin; Göçeri, Evgin; Akan, Aydın; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term.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; Göçeri, Evgin; Dicle, Oğuz; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyA 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.Article Citation - WoS: 45Citation - Scopus: 47A Comparative Performance Evaluation of Various Approaches for Liver Segmentation From Spir Images(Türkiye Klinikleri Journal of Medical Sciences, 2015) Göçeri, Evgin; Ünlü, Mehmet Zübeyir; Göçeri, Evgin; Ünlü, Mehmet Zübeyir; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of seven different pseudo-3D liver segmentation approaches chosen from deterministic (K-means-based), probabilistic (Gaussian model-based), supervised neural network (multilayer perceptron-based), and deformable model-based (level set) segmentation methods. The results of quantitative and qualitative analyses using sensitivity, specificity, and accuracy metrics show that the multilayer perceptron-based approach and a level set-based approach, both of which use distance regularization terms and signed pressure force function, are the most successful methods for liver segmentation from spectral presaturation inversion recovery (SPIR) images. However, the multilayer perceptron-based segmentation method has a higher computational cost. The automatic method using the distance regularized level set evolution with signed pressure force function avoids the sensitivity of a user-defined initial contour for each slice, gives the most efficient results for liver segmentation after the preprocessing steps, and also requires less computational time.