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Öğe Adaptive Wiener-Turbo System and adaptive Wiener-Turbo Systems with JPEG & bit plane compressions(Istanbul Univ, Fac Engineering, 2007) Büyükatak, Kenan; Uçan, Osman Nuri; Göse, Ersin; Osman, Onur; Kent, SedefIn order to improve unequal error protection and compression ratio of 2-D colored images over wireless environment, we propose two new schemes denoted as ''Adaptive Wiener-Turbo System (AW-TS) and Adaptive Wiener-Turbo Systems with JPEG & Bit Plane Compressions (AW-TSwJBC)''. In AW-TS, there is a feedback link between Wiener filtering and Turbo decoder and process iteratively. The scheme employs a pixel-wise adaptive Wiener-based Turbo decoder and uses statistics (mean and standard deviation of local image) of estimated values of local neighborhood of each pixel. It has extra-ordinary satisfactory results of both bit error rate (BER) and image enhancement performance for less than 2 dB Signal-to-Noise Ratio (SNR) values, compared to separately application of traditional turbo coding scheme and 2-D filtering. In AW-TSwJBC scheme, 2-D colored image is passed through a color & bit planes� slicer block. In this block, each pixel of the input image is partitioned up to three main color planes as R,G,B and each pixel of the color planes is sliced up to N binary bit planes, which corresponds to binary representation of pixels. Thus depending on importance of information knowledge of the input image, pixels of each color plane can be represented by fewer number of bit planes. Then they are compressed by JPEG prior to turbo encoder. Hence, two consecutive compressions are achieved regarding the input image. In 2-D images, information is mainly carried by neighbors of pixels. Here, we benefit of neighborhood relation of pixels for each color plane by using a new iterative block, named as ''Adaptive Wiener-Turbo'' scheme, which employs Turbo decoder, JPEG encoder/decoders and Adaptive Wiener Filtering.Öğe Automatic colon segmentation using cellular neural network for colorectal polyps detection(2007) Kılıç, Niyazi; Osman, Onur; Uçan, Osman Nuri; Demirel, KemalIn this paper, an automatic colon segmentation method for Computed Tomography (CT) colonography is presented. Colon segmentation is considered in order to prevent the time consumption while searching polyps out of the colon region and reduce radiologists’ interpretation time. The proposed method is the combination of pre-processing and Cellular Neural Networks (CNN). Also recurrent perceptron learning algorithm (RPLA) is used for CNN training. Original CT images are passed through a threshold and then CNN is used to erase unrelated small objects and smooth sharp corners. It is expected automatic colon segmentation will improve the radiologists’ diagnostic performance.Öğe Bilgisayarli tomografi görüntülerinde kolon bölütleme ve şablon eşleme yöntemi ile kolonik polip tespiti(2008) Kiliç, Niyazi; Osman, Onur; Uçan, Osman NuriBu makalede, bilgisayarlı tomografi (BT) görüntülerinde kolonik poliplerin tespit edilmesi için yeni bir bilgisayar destekli tespit (BDT) sistemi geliştirilmiştir. Önerilen BDT sistemi ile önce hücresel yapay sinir ağları (HYSA) kullanılarak BT görüntülerinden kolon bölgesi çıkarılmıştır. Bölütleme performansının yükseltilmesi için HYSA’nın A, B ve I şablon parametreleri genetik algoritma ile eniyilenmiştir. Her bir BT görüntüsün ilgili kolon bölgeleri bir araya getirilerek üç boyutlu kolon bölgesi görüntüsü oluşturulmuştur. Bu görüntüler üzerinde 4 katmanlı 12 12 × boyutunda üç boyutlu küresel şablonlar çalıştırılarak kolonik polipler tespit edilmiştir. Bu çalışmada; 15 tane kolonik polip içeren 11 hastaya ait toplam 1148 BT görüntüsü değerlendirilmiştir. Önerilen BDT sisteminin tespit duyarlılığı %100 ve hasta başına düşen yanlış pozitif (YP) oranı 10’dur.Öğe Blind equalization of multilevel turbo coded-continuous phase frequency shift keying over MIMO channels(2007) Osman, OnurIn this paper, multilevel turbo coded-continuous phase frequency shift keying (MLTC-CPFSK) is introduced and its bit error performance in multiple input multiple output (MIMO) fading channels are investigated considering a blind maximum likelihood channel estimation. Multilevel turbo coded signals with continuous phase modulation (CPM) provides low spectral occupancy and is suitable for power and bandwidth-limited channels. Besides, antenna diversity is one of the best method to combat with multipath fading effects. The performance of 2LTC for 4-ary CPFSK over AWGN, Rician (for Rician channel parameter K = 10 dB) and Rayleigh channels are given for 1Tx-1Rx, 2Tx-1Rx and 2Tx-2Rx antenna configurations. Channel capacities of 2LTC-4CPFSK signals are obtained as -5.26, -7.65 and -7.14 dB for 1Tx-1Rx, 2Tx-1Rx and 2Tx-2Rx antenna configurations, respectively. Baum-Welch (BW) algorithm is used to estimate the channel parameters. Bit error probabilities of 2 level turbo coded 4 CPFSK (2LTC-4CPFSK) are drawn in the cases of no channel state information (CSI), BW estimation, and perfect CSI. Approximately 0.1 and 0.75 dB gains in Es/N 0 are obtained using BW channel estimator for Rician and Rayleigh channels, respectively. Therefore, MLTC-CPFSK with BW channel estimator has excellent performance in MIMO fading channels. Copyright © 2006 John Wiley & Sons, Ltd.Öğe Blind equalization of space-time-turbo trellis coded/contilluous phase modulation over Rician fading channels(Wiley, 2004) Uçan, Osman N.; Osman, OnurIn this paper. to improve bit error performance and bandwidth efficiency. we combine space-time blockcodes (STBC), turbo trellis codes and continuous phase modulation and denote space-time-turbo trellis coded/continuous phase modulation (ST-TTC/CPM). For high data transmission over wireless fading! channels, STBC provide the maximal possible diversity advantage for multiple decoding algorithm. We present continuous phase modulation (CPM) for ST-TTC signal. mince CPM provided low-Spectral occupancy and is suitable for power and bandwidth-limited channels. In our model. to utilize STBC efficiently. we need to estimate the channel parameters. which influence the signals having continuity property. Therefore, we develop a blind maximum likelihood channel estimation algorithm for signal propagating through a Rician fading channel. Here. Baum-Welch (BW) algorithm based on hidden Markov model (HMM) is modified to provide computationally efficient channel parameter estimation. We also investigate the performance of ST-TTC/CPM in the case of no channel state information (CSI) for various Rician parameters K and Doppler frequency. Copyright (C) 2004 AEI.Öğe Blind equalization of turbo trellis-coded partial-response continuous-phase modulation signaling over narrow-band rician fading channels(2005) Osman, Onur; Uçan, Osman N.In this paper, a blind maximum-likelihood channel estimation algorithm is developed for turbo trellis-coded/continuous-phase modulation (TTC/CPM) signals propagating through additive white Gaussian noise (AWGN) and Rician fading environments. We present CPM for TTC signals, since it provides low spectral occupancy and is suitable for power- and bandwidth-limited channels. Here, the Baum-Welch (BW) algorithm is modified to estimate the channel parameters. We investigate the performance of TTC/CPM for 16-CPFSK over AWGN and Rician channels for different frame sizes, in the case of ideal channel state information (CSI), no CSI, and BW estimated CSI. © 2005 IEEE.Öğe Blind parameter estimation with genetic algorithm in wireless fading channels(İstanbul Ticaret Üniversitesi, 2005) Osman, OnurIn this paper, the performance of genetic algorithm (GA) is presented to estimate the blind channel parameters.8-PSK signals are considered as modulation scheme. Channel is assumed as AWGN and has Rician probability distribution. Furthermore, channel is slow fading channel and channel parameters are assumed to be constant during the transmission of ten signals. Noisy and faded signals are observed from the receiver. GA is applied to estimate the channelparameters. For different fading channels and signalto-noise ratios (SNR), the mean errors of the estimated channel parameters are obtained.Öğe Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching(2008) Ertaş, Gökhan; Gülçür, H.Özcan; Osman, Onur; Uçan, Osman N.; Tunacı, Mehtap; Dursun, MemduhA novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12 × 12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap > 0.85 and misclassification rate < 0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344 slices × 6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity = 100 %), however, there were some false-positive detections (31%/lesion, 10%/slice). © 2007 Elsevier Ltd. All rights reserved.Öğe Channel equalization and noise reduction based on turbo codes(Institute of Electrical and Electronics Engineers Inc., 2003) Büyükatak, Kenan; Göse, Ersin; Uçan, Osman N.; Kent, Sedef; Osman, OnurA new class of error control coding with binary parallel concatenated recursive systematic convolution codes turbo codes- And its an application over SAR (Synthetic Aperture Radar) image transmitting is presented in this paper. The remarkable coding gains achieved by turbo code have a great influence in various area of digital communication, including cellular mobile, deep space communications and satellite communication. The basic idea is that two or more a posteriori probability decoders exchange soft information [I]. One of the decoders calculates the a posteriori probability distribution of the information sequence and passes that information to the next decoder. The new decoder uses this information and computes its own version of the probability distribution. This exchange of information is called an iteration. After a certain number of iterations, a decision is made at the second decoder. For each iteration, the probability that we decode in favor of the correct decision will improve. At the last iteration, the hard decision is made using the soft decision of the last decoder [5]. © 2003 IEEE.Öğe Classification and prediction in data mining with neural networks(2003) Özekes, Serhat; Osman, OnurIn this paper Neural Networks (NN) are drawn in data mining for classification and prediction. Back propagation is used as a learning algorithm. Data mining is one of the hottest current technologies of the information age. As computer systems getting cheaper and its power increases, the amount of collected and processed data available increases. Data mining is a process to discover the patterns and trends in large datasets. In our simulation, financial data set is evaluated. The expectations of bank results and our proposed Neural Network results are compared and some differences are obtained.Öğe Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching(2009) Kılıç, Niyazi; Uçan, Osman N.; Osman, OnurIn this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8×8, 12×12 and 20×20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8×8 cell template. For the 12×12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20×20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient. © 2008 Springer Science+Business Media, LLC.Öğe Combined trellis coded quantization/continuous phase modulation (TCQ/TCCPM)(İstanbul Ticaret Üniversitesi, 2003) Odabaşıoğlu, Niyazi; Osman, Onur; Uçan, Osman NuriIn this paper, we applied Continuous Phase Frequency Shift Keying (CPFSK) to Trellis Coded Quantization/Modulation (TCQ/TCM) and thus we called Trellis Coded Quantization/ Continuous Phase Modulation (TCQ/TCCPM) for this new sys-tem. In this new scheme we use continuous phase frequency shift keying signal set instead of phase shift keying signal set. As an example, an eight state TCQ/TCCPM system is designed and its error performance is evaluated for different Rician fading parameters K and signal to noise ratios.Öğe Computer network optimization using genetic algorithm(2006) Akbulut, Olcay; Osman, Onur; Uçan, Osman NuriIn this paper, Genetic Algorithm (GA) is proposed as optimization software to find the shortest path of various computer networks. It deals with different method concerning the placement routers, routes the packages. The genetic based algorithm defines an optimum way when a computer network system is constructed. Genetic Algorithm gives better results regarding other classical methods as the number of nodes of the network increases.Öğe Computerized detection of architectural distortions in digital mammograms(Elsevier Science Bv, 2005) Özekes, Serhat; Osman, Onur; Çamurcu, A. Yılmaz; Lemke, HU; Inamura, K; Doi, K[Abstract Not Available]Öğe Computerized lung nodule detection using 3D Feature extraction and learning based algorithms(2010) Özekes, Serhat; Osman, OnurIn this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios. To make a decision for each ROI, feed forward neural networks (NN), support vector machines (SVM), naïve Bayes (NB) and logistic regression (LR) methods were used. These methods were trained and tested via k-fold cross validation, and results were compared. To test the performance of the proposed system, 11 cases, which were taken from Lung Image Database Consortium (LIDC) dataset, were used. ROC curves were given for all methods and 100% detection sensitivity was reached except naïve Bayes. © Springer Science + Business Media, LLC 2008.Öğe Concatenation of space-time block codes and turbo trellis coded modulation (ST-TTCM) over Rician fading channels with imperfect phase(2004) Uçan, Osman N.; Osman, OnurIn this paper, the performance of space time-turbo trellis coded modulation (ST-TTCM) is evaluated over Rician and Rayleigh fading channels with imperfect phase. We modify Baum-Welch (BW) algorithm to estimate the fading and phase jitter parameters for multi-antenna configurations. Thus, we assume that the channel parameters change slower than carrier frequency. We know that, at high data rate transmissions over wireless fading channels, space-time block codes (STBC) provide the maximal possible diversity advantage. Here, the combined effects of the amplitude and the phase of the received signal are considered, each one modelled by Rician and Tikhonov distributions, respectively. We investigate space time-turbo trellis coded modulation (ST-TTCM) for 8-PSK for several Rician factor K and phase distortion factor ?. Thus, our results reflect the degradations both due to the effects of the fading on the amplitude and phase noise of the received signal while the channel parameters are estimated by BW algorithm. Copyright © 2004 John Wiley & Sons, Ltd.Öğe Efficient estimation of osteoporosis using Artificial Neural Networks(2007) Lemineur, Gerald; Harba, Rachid; Kılıç, Niyazi; Uçan, Osman N.; Osman, Onur; Benhamou, LaurentIn this communication, Artificial Neural Network (ANN) is applied to discriminate osteoporotic fracture and control cases in a group of 304 patients. ANN is one of the popular methods in optimization of complex engineering problems compared to the classical statistical methods. In our study group, we consider some parameters as inputs: three bone densitometry parameters (HMD) (Femoral neck BMD, Total Body BMD and L2L4 spine BMD), three fractal parameters [1,5] (Hmin. Hmean, Hmax), and age of the patient. We studied three ANN structures with various inputs and hidden neurons. We have reached up to 81.66% correct classification. In comparison we have tested a classical discriminant analysis (Mahalanobis-Fisher) and we only obtained 72% of correct classification. We can conclude that ANN is one of the promising methods in the diagnosis of osteoporosis. ©2007 IEEE.Öğe Forward modeling with forced neural networks for gravity anomaly profile(2007) Osman, Onur; Albora, A. Muhittin; Uçan, Osman N.In this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas - Gürün basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas - Gürün basin, are very close to those obtained from the proposed method. © International Association for Mathematical Geology 2007.Öğe Iterative cellular image processing algorithm(2003) Osman, Onur; Uçan, Osman N.; Albora, A. MuhittinIn this paper, a new iterative image processing algorithm is introduced and denoted as “iterative cellular image processing algorithm” (ICIPA). The new unsupervised iterative algorithm uses the advantage of stochastic properties and neighborhood relations between the cells of the input image. In ICIPA scheme; first regarding to the stochastic properties of the data, all possible quantization levels are determined and then 2D input image is processed using a function, based on averaging and neighborhood relationship, and after that a parameter C is assigned to each cell. Then Gaussian probability values are mapped to each cell regarding to all possible quantization levels and the attended value C. A maximum selector defines the highest probability value for each cell. In the case of complex data, first iteration output is fed into input till a sufficient output is found. We have applied ICIPA algorithm to various synthetic examples and then a real data, the ruins of Hittite Empire. Satisfactory results are obtained. We have observed that de-noising property of our scheme is the best in the literature. It is interesting that the corrupted data with Additive White Gaussian Noise (AWGN) up to 97% ratio, can be de-noised by using our proposed ICIPA algorithm.Öğe Lung nodule diagnosis using 3D template matching(2007) Osman, Onur; Özekes, Serhat; Uçan, Osman N.In this paper, to utilize the third dimension of Computed Tomography, regions of interest (ROI) slices were combined to form 3D ROI image and a 3D template was determined to find the structures with similar properties of nodules. Convolution of 3D ROI image with the proposed template strengthens the shapes similar to the template and weakens the other ones. False-positive (FP) per nodule and per slice versus diagnosis sensitivity were obtained. The Computer Aided Diagnosis system achieved 100% sensitivity with 0.83 FP per nodule and 0.46 FP per slice, when the nodule thickness was greater than or equal to 5.625 mm. © 2006 Elsevier Ltd. All rights reserved.
