Dilek Goksel Duru

Extraction of multiple sclerosis lesions in MR images based on active contours

In this study, a semi automatic segmentation of multiple sclerosis lesions in magnetic resonance (MR) images based on active contours is done. Based on the seed area selection by the user, the parametric active contours which rely on energy minimization, try to shrink and bend the snake resulting in boundary curve (capture region). The method is tested in MATLAB firstly on simulation data, and the precision of internal and external contour extraction is verified. Next, real brain MR images are investigated based on active contours (snakes) for segmentation of lesions. The method is discussed according the active contour implementation findings of the simulation and the real data.

Design and implementation of brain computer interface based on wireless EEG measurements: A pilot study

Brain computer interface (BCI), is a direct communication pathway between brain signals and an external device which often serves to support cognitive and emotional motor functions. The implementation and design of a human computer interface (HCI) based on opened-closed eye movements and arousal - valence measurements are aimed. The goal is to develop a system that working with wireless EEG headset and Arduino board as electronic device or data acquisition based on eye movements and arousal levels. It is a multidisciplinary research area, where computer science, behavioral sciences and several other fields of study are intersecting. The HCI system aimed in this study, it will be useful in rehabilitation research and treatment, and enhance daily life quality of the disabled people. The concept of the study is being used Arduino test kit and wireless EEG sensors. After the adjustment of the hardware components, an embedded code is implemented for the data transmission between the sensor and the Arduino board. Also serial port of the computers is adjusted to listen the transmitted data in order to visualize the sensed movements. This data is projected on a hardware component such as a LED or diode.

Determination of neural fiber connections based on data structure algorithm

The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.

Fiber Tracking: A Recursive Stack Algorithmic Approach

In diffusion tensor magnetic resonance imaging (DT-MRI), each voxel is assigned a tensor that describes local water diffusion. In this study, the eigenvectors and eigenvalues of the diffusion tensor D are analyzed based on stack linked list algorithm. The aim of the study is to develop a reliable and rapid tractography algorithm. In our sample, 60 diffusion weighted human brain images and a null image namely the T2 image creating a set of intensity images of size 256 times 256 times 60 times 30 have been examined. The eigensystem of D is calculated in every pixel, apparent diffusion coefficient ADC is represented with respect to D. The idea of the proposed method is to accomplish the fiber pathway by starting from a single, selected node taking every node in other words all the information of the eigensystem of the whole brain into account. Developing a reliable and rapid fiber tracking algorithm for the clinical use regarding to the verified results is the future study of the work in progress.

Investigation of electrophysiological features during mental workload paradigm

In the last few decades, the relationship between the mental workload and electrophysiological measurements are being studied. The aim of this study is to measure the electrophysiological responses of the autonomic and central nervous system to the increased mental workload. In this concept, backwards counting paradigm is used to increase the mental workload while the brain electrical activity (EEG), hearth rate variability (HRV) and electrodermal activity has been measured synchronously. During the increased mental workload, EEG alpha band suppression and increased EDA were observed. On the other hand, hearth rate variability has not been changed with respect to paradigm.

Investigation of anatomical connectivity of thalamic stroke patients using tract based spatial statistics

Diffusion tensor imaging (DTI) techniques allow the detection of changes in the microstructure of the human brain. The detection and follow-up of the underlying pathology and disruption of tissues is of vital importance. Tract-based spatial statistics (TBSS) is a whole brain voxel-by-voxel technique that allows the statistical comparison of the DTI indexes, restricting the analysis to the center of white matter tracts. In the concept of this study, fractional anisotropic differences of the thalamic stroke patients vs control group are investigated and the tracts are reported.

Analysis of gaze characteristics with eye tracking in elite athletes: A pilot study

Eye movements are essential for natural vision. Eye tracking technology is being used in research in many disciplines to examine the differences in the visual attention of experts and novices. Eye tracking research in sports focuses in the performance of athletes and its relation with perceptual processes. The aim of these studies relies in training the visual behavior of the athletes and to arrange trainings to increase their performance. In this study, the elite group is the Karate-Do players, and the aim is to extract their visual behavioral characteristics and patterns, to explore their gaze strategies, and the differences in their perception, attention and judgement. The experimental paradigm is a Kata and Bunkai video montage from World championships, where the elite athletes are asked to follow the techniques of the players, and the controls, which are interested in Karate-Do but not ever tried it, are asked to follow the movements of the players. During this time the eye tracking is done, and average fixation times, and dwell time in each area of interest have been calculated, and a significant difference between the two groups has been detected. The elite athletes have less fixation counts but longer fixation time compared to controls. So the professionals are not only extracting information from the center, but also from the peripheral areas. This finding is in agreement with the eye tracking literature. Future works will rely in investigating Karate-Do players from varying Karate-Do branches in groupwise comparison.

Source concordance analysis of simultaneously recorded steady state visual evoked potentials and fMRI

1) School of Physical Education and Sport, Sport Health Sciences Department, Marmara University, Istanbul, 34800 2) Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey 3) Department of Neuroscience, Institute of Experimental Medicine, Istanbul University, Istanbul, Turkey 4) Hulusi Behcet Life Sciences Research Laboratory, Istanbul University, Istanbul, Turkey 5) Department of Biomedical Engineering, Istanbul Arel University, Istanbul, 34537 6) Department of Computer Engineering, College Of Engineering And Natural Sciences, Istanbul Sehir University, Istanbul, Turkey

Diffusion tensor fiber tracking based on unsupervised learning

Using Hebbian learning rule and its special case Self-Organizing Map (SOM) as unsupervised learning, a solution is proposed for defining the fiber paths which is a critical problem in diffusion tensor literature, and synthetic diffusion patterns are analyzed by artificial neural network (ANN) approach. Unsupervised learning in training neural networks is a method, where network classification rules are self developed and which does not require any knowledge about the desired output. Only input data is presented to the ANN in the learning process of the network, in other words the input space of the unsupervised learning ANN is the diffusion tensor eigenvector data of each imaging matrix. The network then adjusts the weightings to determine patterns having similar characteristics and classification is done in that way. The resulting classification represents the principal diffusion direction and the weighted diffusion distribution tracked by the fibers. Verification of the application on synthetic data enabled the implementation of the method on real brain images. The aim of the proposed method is to accomplish brain fiber tracking based on learning algorithms according to the modeling studies accepted in artificial neural network literature. Implementing SOM for fiber path discrimination purposes was successful and future work relies in 3D diffusion tensor tractography.

Fiber tracking using recursive stack data structure

In diffusion tensor magnetic resonance imaging (DT-MRI), each voxel is assigned a tensor that describes local water diffusion. In this study, the eigenvectors of the diffusion tensor are analyzed based on stack linked list application. The aim of the study is to develop a reliable and rapid tractography algorithm. The analyzed image sample consists of 60 diffusion weighted human brain images and a null image namely the T2 image creating a set of intensity images of size 256×256×60×30. The eigenvectors of D is calculated in every pixel, apparent diffusion coefficient ADC is represented with respect to D. The idea of the proposed method is to accomplish the fiber pathway by starting from a single, selected node taking every node in other words all the information of the eigenvector of the whole brain into account. Via the proposed study, an elimination method for the main drawback in DTI literature namely the uncertainty regions are aimed.