WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 29Citation - Scopus: 32Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing(Elsevier, 2014-12) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa HakanLiver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.Article Citation - WoS: 22Citation - Scopus: 29Automated Quantification of Immunomagnetic Beads and Leukemia Cells from Optical Microscope Images(Elsevier Sci Ltd, 2019-03) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Yilmaz, BulentQuantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.Conference Object Citation - Scopus: 3İki Durumlu Bir Beyin Bilgisayar Arayüzünde Özellik Çıkarımı ve Sınıflandırma(Institute of Electrical and Electronics Engineers Inc., 2016-10) Altindis, Fatih; Yilmaz, BulentBrain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2017 Elsevier B.V., All rights reserved.Article Citation - WoS: 11Citation - Scopus: 14Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform(World Scientific Publ Co Pte Ltd, 2021) Karakullukcu, Nedime; Yilmaz, BulentPatients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, p, less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.Conference Object Citation - Scopus: 5Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, BulentEmotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.