Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis(2023) Ficici, Cansel; Erogul, Osman; Telatar, Ziya; Kocak, Onur; 0000-0002-8240-4046In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.Item A Clinical Engineering Approach for Design and Management of Central Sterilization Units(2015) Kocak, Onur; Ozgode, Busra; Kocoglu, Arif; Erogul, Osman; 0000-0002-4640-6570; 0000-0003-4803-5504; AAW-3005-2021; AAF-8122-2020Central Sterilization Unit (CSU) are the units that performs sterilization of medical devices, instruments and consumables which used in hospitals and these units are planned to provide services within a quality management system and traceability. The numbers of sterilization procedures are carried out in medium and large scale hospitals, this situation can lead to reduced efficiency of the sterilization process have become critical. In this study, using a medium scale hospital as base, planning to work in coordination with the clinical engineering unit the structure of a central sterilization unit that coordinated to work with clinical engineering unit is recommended. The following issues are discussed in detail: architecture of the CSU, departments, staff, process of monitoring, validation and quality cycles. In addition, contributions to the technical efficiency of the sterilization process from biomedical engineers and technicians which are appointed by the clinical engineering units were examined.Item Detection of PC12 Cell Line Proliferation on AAO Membranes Using Image Processing Techniques(2015) Uyar, Tansel; Erdamar, Aykut; Aksahin, Mehmet Fevzi; Erogul, Osman; Altuntas, Sevde; Buyukserin, Fatih; 0000-0001-8588-480X; AAA-6844-2019Item Obstructive Sleep Apnea Classification with Artificial Neural Network Based On Two Synchronic Hrv Series(2015) Aksahin, Mehmet; Erdamar, Aykut; Firat, Hikmet; Ardic, Sadik; Erogul, Osman; 0000-0001-8588-480X; AAA-6844-2019In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures are used and mean relative absolute error (MRAE) is applied on FFNN results to show affectivities of developed algorithm. The FFNN architectures were trained with various numbers of neurons and hidden layers. The results show that HRV synchronization is directly related to sleep respiratory signals. The CPSD of the HRV series can confirm the clinical diagnosis; both groups determined by an expert physician can be 99% truly classified as a single hidden-layer FFNN structure with 0.0623 MRAE, in which the maximum and phase values of the CPSD curve are assigned as two features. In future work, features taken from different physiological signals can be added to define a single feature that can classify apnea without error.Item Computer-Assisted Diagnosis of Osteoartrithis on Hip Radiographs(2017) Sahin, Seda; Akata, Emin; Erogul, Osman; Tuncay, Cengiz; Sahin, Orcun; Sanal, Hatice TubaItem Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis(2022) Ficici, Cansel; Telatar, Ziya; Erogul, OsmanPsychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other, behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus patients suffering from PNES may be treated with antiepileptic drugs which can have various side effects. Furthermore, seizure is diagnosed after time consuming examination of electroencephalography (EEG) recordings realized by the expert. In this study, automated temporal lobe epilepsy (TLE) patient, PNES patient and healthy subject discrimination method from EEG signals is proposed in order to eliminate the misdiagnosis and long inspection time of EEG recordings. Also, this study provides automated approach for TLE interictal and ictal epoch classification, and TLE, PNES and healthy epoch classification. For this purpose, subbands of EEG signals are determined from discrete wavelet transform (DWT), then classification is performed using ensemble classifiers fed with energy feature extracted from the subbands. Experiments are conducted by trying two approaches for TLE, PNES and healthy epoch classification and patient discrimination. Results show that in the TLE, PNES and healthy epoch classification the highest accuracy of 97.2%, sensitivity of 97.9% and specificity of 98.1% were achieved by applying adaptive boosting method, and the highest accuracy of 87.1%, sensitivity of 86.0% and specificity of 93.6% were attained using random under sampling (RUS) boosting method in the TLE patient, PNES patients and the healthy subject discrimination.