Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article 3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging(Gazi Univ, Fac Engineering Architecture, 2025-02-03) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, BulentThis study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.Conference Object Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi(IEEE, 2015-10) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, EserImage fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.Conference Object Citation - Scopus: 2Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü(Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet YasinEmotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3İki Durumlu Bir Beyin Bilgisayar Arayüzünde Özellik Çıkarımı ve Sınıflandırma(Institute of Electrical and Electronics Engineers Inc., 2016-10) Altindis, Fatih; Yilmaz, BulentBrain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Emotion Recognition Classification in EEG Signals Using Multivariate Synchrosqueezing Transform(IEEE, 2017-10) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentElectrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature.Conference Object Citation - Scopus: 2Emotional State Sensing by Using Hybrid Multivariate Empirical Mode Decomposition and Synchrosqueezing Transform(IEEE, 2018-11) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentIn recent years, utilizing Hilbert-based time frequency methods in emotional state sensing research attracted attention in the brain computer interfaces. Primarily, Hilbert Transform-based empirical mode decomposition (EMD) was found to be suitable for emotional state modeling studies. In more recent studies, models of emotional state recognition were proposed in which the classification was implemented by using the features obtained after applying the time, frequency, and time frequency domain methods to intrinsic mode functions achieved by operating EMD. In this study, an analysis of emotional state recognition is proposed by using the features of the synchrosqueezing coefficients obtained in the classification process after applying the Synchrosqueezing Transform to intrinsic mode functions achieved by using Multivariate EMD. As a result, EEG data available in the DEAP database were categorized as low and high for valence, activation, and dominance dimensions, and 4 different classifiers were utilized in the classification process. The most satisfying ratios of valence, activation and dominance were attained 76%, 68%, and 68% respectively.Conference Object Emotion Elicitation Analysis in Multi-Channel EEG Signals Using Multivariate Empirical Mode Decomposition and Discrete Wavelet Transform(IEEE, 2017-10) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentIn recent years, wavelet-based, Fourier-based and Hilbert-based time-frequency methods attracted attention in emotion state classification studies in human machine interaction. In particular, the Hilbert-based Empirical Mode Decomposition and Wavelet-based Discrete Wavelet Transform have found applications in emotional state analysis. In this study, a model of emotional elicitation is proposed in which the classification is made by using the features of the wavelet coefficients obtained after applying the Discrete Wavelet Transform to IMFs achieved by using Multivariate Empirical Mode Decomposition. Accordingly, EEG data available in the DEAP database were classified as low / high for valence, activation, and dominance dimensions, and 4 different classifiers were used in the classification phase. The best ratios of valence, activation and dominance were obtained ideally 70.1%, 58.8%, 60.3% respectively.Conference Object Kolonoskopi Görüntülerinde Bilineer İnterpolasyonun Tekstör Analizine Etkisi(Institute of Electrical and Electronics Engineers Inc., 2017-10) Kacmaz, Rukiye Nur; Yilmaz, BulentInterpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32×32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections. © 2018 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Beyin Bilgisayar Arayüzü Uygulamalari için Dinlenme, Harekete Niyet ve Hareket Ayırma(Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, BulentBrain-computer interface (BCI) is a system that provides a means to control prosthesis, wheelchair, or similar devices using brain waves without direct motor nervous system involvement. For this purpose, brain waves obtained from multiple electrodes placed on the scalp (EEG, Electroencephalogram) are used. Emotiv Epoc used to obtain EEG signals is a low-cost device and has real-time applications. The aim of this study is the detection of rest, imagination and real movement using EEG signals obtained by Emotiv Epoc headset. As a result, As a result, the data obtained from 39 trials from a female subject were classified resting, motion imagination and movement, according to 97.4% accuracy by using the statistical features of distortion, logarithm energy entropy, energy, Shannon entropy and kurtosis. In this study, it has been shown that this system can be remarkably successful for BCI applications. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Yapısal Benzerlik İndeksini Kullanarak Kolonoskopi Videolarında Değişim Anlarının Belirlenmesi(IEEE, 2018-11) Kacmaz, Rukiye Nur; Yilmaz, BulentThe aim of this study is to reduce the number of images extracted from the videos recorded by the specialists during the colonoscopy process for further examination, thereby enabling the specialist to deal with fewer images. Since the images obtained from the videos are very similar, the main assumption of this study is that the whole video can be represented by fewer images. The approach used in this study is the structural similarity index. Totally, images were obtained from 4 different videos coming from healthy, ulcerative colitis, Crohn's, and polyp patients. The noisy images in these videos were eliminated manually. When the structural similarity index between two consecutive clear images was less than 0.83, the second image was selected and shown to the specialist for his/her examination. By this way, the frames carrying significantly new information from the videos were defined as the variation instances. The tests on healthy or diseased colon videos showed that only 5-10% of the clear images provide significantly new information.