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 Automatic Vascular Segmentation on Angio Images(2017) Akshain, Mehmet Feyzi; Ozen, S. Kutay; Eren, Neyyir TuncayCardiovascular disease is one of today's major health problems. These diseases are the result of constriction or blockage of coronary vessels feeding heart. Diagnosis of Cardiovascular contraction is determined visually by physicians with angio imaging method. Visually defined vascular diseases may give subjective results. Vessel constrictions in angiograms are automatically determined by vascular segmentation to help physicians diagnose cardiovascular stenosis and to minimize subjective results. In present study, adaptive thresholding method and frangi filter were applied to the pre-processed angiogram images. After this application, the noise cleaning method on the determined cardiovascular image was performed and the vein structure was detected with high accuracy rate and low calculation time.