Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 1Decoding and Predicting the Attributes of Urban Public Spaces With Soft Computing Models and Space Syntax Approaches(Education and research in Computer Aided Architectural Design in Europe, 2023) Yönder, Veli Mustafa; Doğan, Fehmi; Çavka, Hasan Burak; Tayfur, Gökmen; Dülgeroğlu, ÖzümPeople spend a considerable amount of time in public spaces for a variety of reasons, albeit at various times of the day and during season. Therefore, it is of utmost importance for both urban designers and local authorities to try to gain an understanding of the architectural qualities of these spaces. Within the scope of this study, squares and green parks in Izmir, the third largest city in Turkey, were analyzed in terms of their dimensions, landscape characteristics, the quality of their semi-open spaces, their landmarks, accessibility, and overall aesthetic quality. Using linear predictor, general regression neural networks, multilayer feed-forward neural networks (2-3-4-5-6 nodes), and genetic algorithms, soft computing models were trained in accordance with the results of the conducted analyses. Meanwhile, using space syntax methodologies, a visibility graph analysis and axial map analysis were conducted. The training results (i.e., root mean square error, mean absolute error, bad prediction rates for testing and training phases, and standard deviation of absolute error) were obtained in a comparative table based on training times and root mean square error values. According to the benchmarking table, the network that most accurately predicts the aesthetic score is the 2-node MLFNN, whereas the 6-node MLFN network is the least successful network. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.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; Dicle, OğuzDeveloping 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.