Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 14Citation - Scopus: 18Separating Normosmic and Anosmic Patients Based on Entropy Evaluation of Olfactory Event-Related Potentials(Elsevier Ltd., 2019) Güdücü, Çağdaş; Olcay, Bilal Orkan; Schaefer, L.; Aziz, M.; Schriever, V. A.; Özgören, Murat; Hummel, T.Objective: Methods based on electroencephalography (EEG) are used to evaluate brain responses to odors which is challenging due to the relatively low signal-to-noise ratio. This is especially difficult in patients with olfactory loss. In the present study, we aim to establish a method to separate functionally anosmic and normosmic individuals by means of recordings of olfactory event-related potentials (OERP) using an automated tool. Therefore, Shannon entropy was adopted to examine the complexity of the averaged electrophysiological responses. Methods: A total of 102 participants received 60 rose-like odorous stimuli at an inter-stimulus interval of 10 s. Olfactory-related brain activity was investigated within three time-windows of equal length; pre-, during-, and post-stimulus. Results: Based on entropy analysis, patients were correctly diagnosed for anosmia with a 75% success rate. Conclusion: This novel approach can be expected to help clinicians to identify patients with anosmia or patients with early symptoms of neurodegenerative disorders. Significance: There is no automated diagnostic tool for anosmic and normosmic patients using OERP. However, detectability of OERP in patients with functional anosmia has been reported to be in the range of 50%.Article Citation - WoS: 5Citation - Scopus: 7High-Speed Dynamic Atomic Force Microscopy by Using a Q-Controlled Cantilever Eigenmode as an Actuator(Elsevier Ltd., 2015) Balantekin, MüjdatWe present a high-speed operating method with feedback to be used in dynamic atomic force microscope (AFM) systems. In this method we do not use an actuator that has to be employed to move the tip or the sample as in conventional AFM setups. Instead, we utilize a Q-controlled eigenmode of an AFM cantilever to perform the function of the actuator. Simulations show that even with an ordinary tapping-mode cantilever, imaging speed can be increased by about 2 orders of magnitude compared to conventional dynamic AFM imaging.Article Citation - WoS: 16Citation - Scopus: 20Automated Labelling of Cancer Textures in Colorectal Histopathology Slides Using Quasi-Supervised Learning(Elsevier Ltd., 2013) Önder, Devrim; Sarıoğlu, Sülen; Karaçalı, BilgeQuasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results.Article Citation - WoS: 21Citation - Scopus: 26Computerized Method for Nonrigid Mr-To Breast-Image Registration(Elsevier Ltd., 2010) Ünlü, Mehmet Zübeyir; Krol, A.; Magri, A.; Mandel, J. A.; Lee, W.; Baum, K. G.; Lipson, E. D.; Coman, I. L.; Feiglin, D. H.We have developed and tested a new simple computerized finite element method (FEM) approach to MR-to-PET nonrigid breast-image registration. The method requires five-nine fiducial skin markers (FSMs) visible in MRI and PET that need to be located in the same spots on the breast and two on the flanks during both scans. Patients need to be similarly positioned prone during MRI and PET scans. This is accomplished by means of a low gamma-ray attenuation breast coil replica used as the breast support during the PET scan. We demonstrate that, under such conditions, the observed FSM displacement vectors between MR and PET images, distributed piecewise linearly over the breast volume, produce a deformed FEM mesh that reasonably approximates nonrigid deformation of the breast tissue between the MRI and PET scans. This method, which does not require a biomechanical breast tissue model, is robust and fast. Contrary to other approaches utilizing voxel intensity-based similarity measures or surface matching, our method works for matching MR with pure molecular images (i.e. PET or SPECT only). Our method does not require a good initialization and would not be trapped by local minima during registration process. All processing including FSMs detection and matching, and mesh generation can be fully automated. We tested our method on MR and PET breast images acquired for 15 subjects. The procedure yielded good quality images with an average target registration error below 4 mm (i.e. well below PET spatial resolution of 6-7 mm). Based on the results obtained for 15 subjects studied to date, we conclude that this is a very fast and a well-performing method for MR-to-PET breast-image nonrigid registration. Therefore, it is a promising approach in clinical practice. This method can be easily applied to nonrigid registration of MRI or CT of any type of soft-tissue images to their molecular counterparts such as obtained using PET and SPECT. © 2009 Elsevier Ltd. All rights reserved.Article Citation - WoS: 36Citation - Scopus: 44Continuous Time Wavelet Entropy of Auditory Evoked Potentials(Elsevier Ltd., 2010) Çek, Mehmet Emre; Özgören, Murat; Savacı, Ferit AcarIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time. © 2009 Elsevier Ltd. All rights reserved.