Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Department: search.filters.department.Abdullah Gül UniversitySubject: search.filters.subject.Segmentation

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  • Article
    Frequency-Based Deep Occlusion Awareness Instance Segmentation
    (MDPI, 2026-02-26) Guzel, Yasin; Aydin, Zafer; Talu, Muhammed Fatih
    One major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors-renowned for their strong representational capacity-with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet-Thr, FUNet-BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models' performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 12
    Not All Emerging Markets Are the Same: A Classification Approach With Correlation Based Networks
    (Elsevier Science inc, 2017-12) Sensoy, Ahmet; Ozturk, Keyser; Hacihasanoglu, Erk; Tabak, Benjamin M.
    Using dynamic conditional correlations and network theory, this study brings a novel interdisciplinary framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from January 2003 to December 2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the unweighted average of dynamic conditional correlations between cross country bond returns significantly increases in phase 2. At first glance, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, using correlation based stable networks, we show that this is not enough to make such a strong conclusion. In particular, we reveal that the increased average correlation is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Potential reasons for the post-crisis segmentation and important implications for international investors and policymakers are discussed. (C) 2016 Elsevier B.V. All rights reserved.
  • Article
    A Comparative Study of Unet Variants for Low-Grade Glioma Segmentation in Magnetic Resonance Imaging
    (Inonu University, 2025-06-25) Guzel, Yasin; Aydin, Zafer
    Brain tumors originating from glial cells are pathological entities that significantly impact quality of life and are classified based on their malignancy into low-grade gliomas (LGGs) and high-grade gliomas (HGGs). While the more aggressive HGGs have been extensively studied, LGGs are of critical importance for early diagnosis due to their potential progression to HGGs if left untreated. This has driven researchers to develop methods for the rapid and consistent diagnosis of LGGs. In this study, three models—UNet, Transformer UNet, and Super Vision UNet—were comparatively evaluated for the automatic segmentation of LGGs using magnetic resonance imaging (MRI) data. Multimodal MRI scans from 110 patients, retrieved from The Cancer Imaging Archive (TCIA), were used to train the models. Performance was evaluated using Dice Coefficient, Tversky Index, and Intersection over Union (IoU) metrics. The Super Vision UNet achieves the highest Dice (0.9115) and Tversky (0.9154) scores, while the Transformer UNet attains the highest IoU (0.8789). Both advanced models demonstrate superior segmentation performance with lower loss values compared to the conventional UNet. Visual outputs indicate that the modern architectures delineate tumor contours with greater precision. These results highlight the effectiveness and reliability of contemporary UNet-based and Transformer-based architectures in segmenting complex tumor structures such as LGGs. Integrating these models into clinical decision support systems holds promise for enhancing the speed and accuracy of the diagnostic process. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 1
    Comparison of Lung Tumor Segmentation Methods on PET Images
    (IEEE, 2015-10) Eset, Kubra; Icer, Semra; Karacavus, Seyhan; Yilmaz, Bulent; Kayaalti, Omer; Ayyildiz, Oguzhan; Kaya, Eser
    Lung cancer is the most common cause of cancer-related deaths that occur all over the world. Recently, various image processing approaches have been used on PET images in order to characterize the uniformity, density, coarseness, roughness, and regularity (i.e., texture properties) of the intratumoral F-18-fluorodeoxyglucose (FDG) uptake. The first and important step of this kind of analysis is to differentiate tumor region from other structures and background, which is called segmentation. In this study, k-means, active contour (snake), and Otsu's tresholding methods were applied on PET images obtained from 36 patients and the performances were compared by the nuclear medicine expert in our team. The results show that Otsu tresholding approach is more selective.