Browsing by Author "Ozen, Sinasi Kutay"

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Automatic Brain Tissue Segmentation on TOF MRA Image
(2020) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of live steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions arc detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.
Automatic Glacoma Detection Using Whale Optimization and Support Vector Machines
(2022) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
Glaucoma is among the most common causes of permanent blindness in humans. The mass screening will aid in early diagnosis in a large population, as the initial symptoms are not obvious. This type of mass screening requires an automated diagnostic technique. Our proposed automation extracts feature by obtaining disk-to-cup ratio by applying histogram equalization, median filter, otsu thresholding, and whale optimization algorithm, respectively, on the optic disc region obtained by preprocessing. In addition, the optic disc circumference, optic disc area, optic cup circumference, and optic cup area values obtained from the optic disc region are given to the support vector machine model together with the cup-disc ratio, and glaucoma detection is made automatically. The proposed system has been validated on a real ophthalmological images of both normal and glaucoma cases. The results show the effectiveness of the proposed method when compared with other existing systems.
Neurofeedback System Design Based On Discrete Wavelet Transform
(2018) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
Neurofeedback is a learning strategy that helps to alter brain waves called electroencephalography (EEG). Neurofeedback technique is used in the treatment of various diseases such as epilepsy, sleep disorders, attention deficit and learning disability. In the first stage of this study, a device was developed in which the EEG signals are collected and transferred to the computer. In the second stage of the study, the obtained EEG signals were analyzed by the discrete wavelet transform method, and according to the analysis result, a system was designed by which the patients could be treated using visual feedback. The usability of the visual feedback part based on theta/ beta protocol with EEG subbands has been tested in real time.