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Öğe Classification of transcranial Doppler signals using their chaotic invariant measures(ELSEVIER IRELAND LTD, 2007) Ozturk, Ali; Arslan, AhmetIn this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases. (c) 2007 Elsevier Ireland Ltd. All rights reserved.Öğe Comparison of neuro-fuzzy systems for classification of transcranial Doppler signals with their chaotic invariant measures(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Ozturk, Ali; Arslan, Ahmet; Hardalac, FiratTranscranial Doppler (TCD) is a non-invasive diagnosis method which is used in diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. In this study, chaos analysis of the TCD signals recorded from the middle arteries of the temporal region of the brain of 82 patients and 24 healthy people was investigated. Among 82 patients, 20 of them had cerebral aneurism, 10 patients had brain hemorrhage, 22 patients had cerebral oedema and the remaining 30 patients had brain tumour. It was found that all of the TCD signals represented nonlinear dynamics and had an underlying low-level determinism. All of the TCD signals were passed through the nonlinearity tests which involved the application of surrogate data method. The maximum Lyapunov exponent (lambda(1)) which is the strongest quantitative indicator of chaos was found to be positive for all TCD signals. The correlation dimension (D-2) was found as greater than 2 and as fractional value for all TCD signals. This result indicates that the nonlinear dynamics of the TCD signals corresponds to a strange attractor in phase space which implies a non-ergodic dissipative system having low-level chaotic behaviour. Besides, the values of lambda(1) and D-2 were approximately the same for the TCD signals of the patients having the same brain disease. Relying on this observation, these two chaotic invariant measures were divided into training and test subsets including 52 and 54 subjects, respectively. For comparison purposes, the training set was used to build two different neuro-fuzzy models, namely ANFIS and NEFCLASS. The rule base of the NEFCLASS model was created by applying the samples in the training subset for 1000 epochs. On the other hand, the ANFIS model was trained for 250 epochs until the convergence error has decreased to 0.42 x 10(-5). The ANFIS model achieved better classification accuracy than the NEFCLASS model for the samples in the test set. The classification accuracy of the ANFIS model after training was 94.40% whilst this value was found as 88.88% for the NEFCLASS model. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Elastic-plastic stress analysis in a long functionally graded solid cylinder with fixed ends subjected to uniform heat generation(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ozturk, Ali; Gulgec, MufitElastic-plastic deformation of a solid cylinder with fixed ends, made of functionally graded material (FGM) with uniform internal heat generation is investigated, based on Tresca's yield criterion and its associated flow rule, considering four of the material properties to vary radially according to a parabolic form. These four material properties are yield strength, modulus of elasticity, coefficients of thermal conduction and thermal expansion, assumed to be independent of temperature as Poisson's ratio which is taken as constant. The materials which compose the functionally graded cylinder are supposed to be elastic-perfectly plastic materials. Expressions for the distributions of stress, strain and radial displacement are found analytically in terms of unknown interface radii. After determining these radii numerically by means of Mathematica 5.2, the distributions are plotted versus dimensionless radius, increasing heat generation, to compare the FGM cylinder with the homogeneous one. The numerical values used in this work for material parameters are arbitrarily chosen to point out the effect of the non-homogeneity on the stress distribution. The results obtained show that the stress distribution, as well as the development of plastic region radii, is influenced substantially by the material non-homogeneity. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Neuro-fuzzy Classification of Transcranial Doppler Signals with Chaotic Meaures and Spectral Parameters(IEEE, 2015) Ozturk, Ali; Arslan, AhmetTranscranial Doppler (TCD) is a non-invasive diagnosis method which is used in diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. In this study, chaos analysis of the TCD signals recorded from the middle arteries of the temporal region of brain of the 82 patients and 23 healthy people was investigated. Among 82 patients, 20 of them had cerebral aneurism, 10 had brain hemorrhage, 22 had cerebral oedema and the remaining 30 had brain tumor. Maximum Lyapunov exponent which is the strongest quantitative indicator of chaos was found to be positive for all TCD signals. The correlation dimension was found as greater than 2 and as fractional value for all TCD signals. These two features were used for training a NEFCLASS model. The NEFCLASS model had two input nodes for D2 and maximum Lyapunov exponent values and five output nodes representing the subject group to which the inputs belonged. In order to make k-fold cross-validation, the data set was randomly divided into 5 subsets of equal size. In an iterated manner, 4 of these subsets were used for training and the remaining 1 subset was used for testing. This operation was repeated for 3 times. The average accuracy for train and test set was found as %81 and %79, respectively. The performance of the NEFCLASS model was also assessed in the same manner with spectral parameters (i.e. resistivity index and pulsatility index) which were obtained from Doppler sonograms. The average accuracy was found as %67 and %63 for train and test set, respectively.Öğe Texture segmentation with seeded region growing in feature space by integrating boundary information(IEEE, 2006) Ozturk, Ali; Arslan, Ahmet]In this study, region-based segmentation of textural images is investigated. For this purpose, the seeded region growing algorithm is used in feature space. In order to make an accurate segmentation, it is crucial to appropriately select the initial seed points as well as to decide where to stop the growing procedure. In the first stage, the boundaries between the textures that will guide the growing process are extracted. Then, the initial seed points are selected according to some intra-region similarity and inter-regional distance criteria in the feature space. At the end of the region growing, the smaller regions are merged according to the boundary information to construct the final segmented image. To discriminate between textures, four different features are used. The first three features are the fractal dimension (FD) of original image, constrast-strecthed image and top-hat transformed image, respectively. The fourth feature is the entropy which is a parameter obtained from the spatial gray-level co-occurence matrix of the image. The experimental results are presented for mosaics with different number of textures.
