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Öğe 3D ELECTRONIC BRAIN ATLAS MODEL FOR THE DETECTION OF NEUROLOGICAL DISORDERS(ST JOHN PATRICK PUBL, 2017) Ozic, Muhammet Usame; Ozsen, Seral[Abstract not Available]Öğe Atlas-Based Segmentation Pipelines on 3D Brain MR Images: A Preliminary Study(EDUSOFT PUBLISHING, 2018) Ozic, Muhammet Usame; Ekmekci, Ahmet Hakan; Ozsen, SeralThree dimensional structural MR imaging is a high-resolution imaging technique used in the detection and follow up of neurological disorders. Rigid changes in the brain are usually interpreted and reported manually by radiologists using MR images. The results of manual interpretation may vary with respect to the experts. At the same time, measurement and segmentation of the brain regions and the manual evaluation of the volume changes are a difficult process. With the increase of numerical methods, automated and semi-automated package programs have been developed for the analysis of brain measurements. These programs use electronic brain atlases or tissue probability maps. However, since the package programs have a lot of analysis time and give only certain outputs, they may be disadvantaged in the use of segmentation and measurement of brain regions. Hence, special pipelines are needed especially to obtain valuable features for artificial intelligence and classification studies. In this study, we propose pipelines to segment 3D certain brain regions, which will help to find the basic features such as volume changes, intensity variations, symmetry deteriorations, and tissue changes. With these pipelines, 3D segmentation of the brain regions defined in the atlas can be performed and normalized. It is aimed to use these studies as a preliminary study in order to quantitatively determine the basic changes in the brain by performing the volume of interest methods and to formulate a decision support system.Öğe Classification of Dementia with ANN using Multiple Variable(IEEE, 2015) Ozic, Muhammet Usame; Ozbay, Yuksel; Ekmekci, Ahmet HakanDementia is a stage that identifies all of the symptoms that led to the weakening of multiple cognitive functions. It is usually expressed as "bunama" among people. Dementia is not a disease itself is known as a transition period that defines the initial stages of many diseases. Proportional change in mental function of many variables that influence causes our body causing this stage, textural and volume losses which may occur in the brain, the person may be counted as demographic and clinical variables. Prediction of disease with a combination of variables is one of the popular sizes in recent topics in the literature. In this study received the demented patients from Open Access Series of Imaging Studies (OASIS) database structural magnetic resonance (MR) imaging features and use in combination with the demographic data of the patients was investigated in artificial neural networks classification performance.Öğe Detection of Tumor With Otsu-PSO Method On Brain MR Image(IEEE, 2014) Ozic, Muhammet Usame; Ozbay, Yuksel; Baykan, Omer KaanMultiple image thresholding is a popular method used to separate homogeneous subsets of gray level images. To find the optimum threshold in the image in the literature is still a research topic. Many image thresholding method uses the histogram of the image. In this study, the objective function of Otsu method which is a statistical process, Particle Swarm Optimization with an intuitive algorithm (PSO) by maximizing, the optimal threshold values on a medical image were studied to find. The values obtained were tested with a standard test image and brain magnetic resonance (MR) image exposed on the tumor region in segmentation, Otsu-PSO method performance was monitored.Öğe Hyperemesis Gravidarum and Cerebral Electrophysiology Determination of Cerebral Localization through Electroencephalography Signal Processing(TURKISH NEUROLOGICAL SOC, 2016) Ekmekci, Hakan Ahmet; Yilmaz, Arzu Setenay; Ozic, Muhammet Usame; Ozbay, Yuksel; Kerimoglu, Ozlem Secil; Celik, Cetin; Ozturk, SerefnurObjective: Hyperemesis gravidarum (HG) is a disease characterized by excessive vomiting and nausea during pregnancy. It differs from normal pregnancy where simple nausea and vomiting are seen frequently with unknown cause. The place and role of the brain in HG is unknown. Materials and Methods: Thirty-three healthy pregnant women and 30 patients diagnosed with HG admitted to Selcuk University Faculty of Medicine, Obstetrics and Gynecology Department were included and electroencephalograph (EEG) signals of all patients obtained at Neurology Department were examined. These signals were evaluated with high math and examined with developed engineering methods. The sampling frequency of the EEG was 200 Hz. Data were obtained in the frequency-power axis using 0.1 Hz frequency resolution, Hamming windowing, and 0.5 overlap ratio with signals on the time axis on all channels. All sub-bands have formed with unearthed power spectral density as delta, theta, alpha, and beta and after being created was calculated spectral densities. Results: As a result, while showing significant changes as delta band for Fp1F3, theta band for C3P3, F3C3, Fp1F3, P3O1, T5O1, for other channels and sub-bands has not seen any significant changes with regard to average power spectral density. Conclusion: HG and normal pregnancies, when examined in terms of power spectral density, abnormalities were observed in the EEG signals in the left hemisphere frontal area of the delta band, fronto-centro-parietal, and parietal-occipital areas of the theta band. In light of the literature, neither cerebral abnormalities in HG could be displayed nor the place of abnormality could be shown. However, this study is the first to clearly show abnormalities of theta-delta band activity and differences of locations in the left cerebral hemisphere.Öğe A New Model to Determine Asymmetry Coefficients on MR Images using PSNR and SSIM(IEEE, 2017) Ozic, Muhammet Usame; Ozsen, SeralThe human brain consists of two hemispheres, right and left. These two hemispheres are almost symmetrical, not perfectly. However, in neurological diseases, the volumetric losses in the brain begin to deteriorate asymmetrically between the two hemispheres. This deterioration can be local or global in the brain. Symmetry deterioration can be a biomarker in the early stage diagnosis and the following of neurological diseases. However, it has been stated that the analysis of asymmetry in the brain by numerical methods is problematic. In this study, a new approach is proposed to analyze the brain symmetry deterioration numerically. In order to perform asymmetry analysis in MR images, two hemispheres must be separated from each other by finding the midsagittal plane which are known symmetry axis. The PSNR and SSIM coefficients are often used for quality measurements between two images. In the study, these coefficients were tested for asymmetry measurement. Statistical analysis was performed by determining PSNR-SSIM coefficients between 70 Control and 70 Alzheimer Disease MR images from the OASIS database. It was determined that the use of PSNR and SSIM coefficients in the asymmetry analysis of MR images gave meaningful results.Öğe A Novel Feature Extraction Approach with VBM 3D ROI Masks on MRI(SPRINGER-VERLAG SINGAPORE PTE LTD, 2017) Ozic, Muhammet Usame; Ozsen, Seral; Ekmekci, Ahmet HakanAlzheimer's disease is a neurological disorder that usually starts with aging. Alzheimer's disease is a serious health and economic burden on governments, along with an increase in elderly population in developed and developing countries. There is no known cause of this disease and there is no treatment. For this reason, early diagnosis of the disease, socioeconomic and psychological outputs and medical treatments are still a hot topic investigated in the world. Magnetic Resonance Imaging is one of the medical imaging techniques that show the progression of Alzhiemer in brain. Brain deterioration and volume loss of the disease first begins with memory regions and then spreads to other brain regions. If atrophy is observed and detected by manual methods, it may not be seen due to user dependency, operator error and inexperience. For these reasons, automatic, numerical and atlas-based methods are being developed for the observation and capture of neurological diseases. In this study, 99 Alzheimer patients and 99 normal control MR images were analyzed using Voxel Based Morphometry, one of the numerical methods of atrophy observations in Magnetic Resonance Imaging. Losses in the brain were then produced as three-dimensional binary masks. Using these masks, normalized segmented, modulated normalized segmented, and normalized images that were stripped from the non-brain structures were masked. Histogram based first order statistical features were extracted in the masked areas. The efficany of this technique was statistically compared between Alzheimer's and normal control. MR images have been downloaded freely from the OASIS database.Öğe T-test Feature Ranking Based 3D MR Classification with VBM Mask(IEEE, 2017) Ozic, Muhammet Usame; Ozsen, SeralThe monitoring of volumetric changes in the brain during neurological diseases is done with three dimensional structural MR images. Numerical methods are needed to evaluate the volumetric difference between healthy and diseased MR image groups. Voxel based morphometry is a numerical method used to perform inter-group and intra-group analysis of MR images. With this method, the volume differences between the groups can be analyzed in the coordinate dimension. In this study, volumetric differences between Alzheimer and Normal MR images were investigated by voxel based morphometry. From these differences, three dimensional volume loss masks of groups were obtained. The mask of loss was masked with the gray matter area where the basic volume losses of Alzheimer's begin. In each gray matter image, the voxel values corresponding to the same coordinates as the loss mask were transformed into a vector. The voxel values corresponding to the same point as the loss point were ranked by t-test and the most significant coordinate values were ranked. After this ranking, classification was done with support vector machines using voxel values. As a result of this model, the number of voxel coordinates giving the best classification was determined and the performance of the method was measured.Öğe Voxel Based Morphometric Analysis on MR Images(IEEE, 2017) Ozic, Muhammet Usame; Ozsen, Seral; Ekmekci, Ahmet HakanExamining of progress of diseases that occur in brain using numerical methods is one of the topics of neuroscience researches. One of the developing numerical methods for examinig local and global changes that occur in brain is Voxel Based Morphometry. For examining intragroup and intergroup differences via Voxel Based Morphometry is required to use some preprocessing methods together with statistical tools. In this study, Voxel Based Morphometry method have been examined using labeled Normal Control and Alzheimer Disease. Magnetic Resonanse images were taken from Open Access Series of Imaging Studies database. After examining volumetric differences as statistical that occur in the brain between two groups via Voxel Based Morphometry, it has been mapped on the template atlases.
