Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 2Citation - Scopus: 3Handwriting Recognition by Derivative Dynamic Time Warping Methodology Via Sensor-Based Gesture Recognition(Maejo University, 2022) Tunçer, Esra; Ünlü, Mehmet ZübeyirA handwritten character recognition methodology based on signals of acceleration obtained from gesture sensors with dynamic time warping (DTW) is presented. After applying the preprocessing steps of filtering, character separation and normalisation, similarities are detected by DTW and each signal component corresponding to a character is classified. However, the nature of the writing process may induce additional time-shifting problems among repetitions of characters since DTW uses only the amplitude values of signals to calculate the distance between them. Accordingly, when signals have different acceleration and deceleration values, irrelevant points of the signals may match each other just because their amplitude values are close. To overcome this problem, derivative dynamic time warping (DDTW) methodology is also implemented. The methodologies mentioned as well as the linear alignment approach were tested with Euclidean, Manhattan and Chessboard distance metrics to detect user-dependent/independent acceleration signals of lower-case characters of the English alphabets and digits. Recognition accuracy rates of Euclidean and Chessboard metrics with DDTW are 98.65%, which is the highest value among all methods applied and metrics. The comparison of Euclidean and Chessboard durations shows that Chessboard with DDTW is the most efficient method in terms of time.Article A Saliency-Weighted Orthogonal Regression-Based Similarity Measure for Entropic Graphs(Springer, 2019) Ergün, Aslı; Ergün, Serkan; Ünlü, Mehmet Zübeyir; Güngör, CengizVarious measures are used to determine similarity ratios among images before and after image registration. Image registration methods are based on finding the translation, rotation, and scaling parameters that maximize the similarity between two images by taking advantage of the feature points and densities that are found. While the similarity criterion is calculated, it is possible and advantageous to use approximation methods on the graphs based on information theory. The current study proposes a new similarity measure based on saliency-weighted orthogonal regression derived from the weighted sums of the saliency map of the orthogonal regression residuals formed on the entropic graph. It is evaluated in terms of both quantitative and qualitative methods and compared with other graph-based similarity measures.Conference Object İskelet Dal Noktaları Kullanan Entropik Çizgelerde Çakıştırma ve Optimizasyon(Institute of Electrical and Electronics Engineers Inc., 2017) Ergün, Aslı; Ergün, Serkan; Ünlü, Mehmet Zübeyir; Güngör, CengizGörüntü çakıştırma işleminde görüntülerin ne kadar benzediklerinin ve iki görüntü arasındaki benzerliği maksimuma getiren kayma, dönme ve ölçeklendirme dönüşüm parametre değerlerinin bulunması gerekmektedir. Benzerlik ölçütü ve buna bağlı parametreler hesaplanırken entropik çizge diye adlandırılan, bilgi teorisi tabanlı ölçütlerin çizge üzerinde yakınsama yöntemleri kullanılabilir. Bu çalışmada, farklı entropik çizgeler üzerinde benzerlik ve optimizasyon ölçütleri karşılaştırılmış ve çizge oluşturmak için iskelet dal öznitelik noktalarının kullanılmasının başarılı sonuçlar verdiği görülmüütür.Article Citation - WoS: 1Citation - Scopus: 2Dinamik Zaman Bükme Metodu Kullanarak İvmeölçer Tabanlı El Yazısı Karakter Tanıma(Institute of Electrical and Electronics Engineers Inc., 2016) Tunçer, Esra; Ünlü, Mehmet ZübeyirBu çalışmada, ivmeölçer kullanılarak el yazısı ile yazılan karakterlerin tanınması yapılmıştır. Karakter tanıma çalışmalarında genellikle kullanılan görüntü işleme teknikleri yerine, bu projede yazıyı yazan kişinin yazma hareketlerinden elde edilen veriler kullanılmıştır. Kişinin yazıyı yazma hareketlerini elde edebilmek için 3 eksenli ivmeölçer kullanılmış ve buradan elde edilen verilerle karakterler Dinamik Zaman Bükme yöntemiyle tanınmıştır. İvmeölçer ile elde edilen veriler genellikle gürültülü veriler olduğundan verilere tanıma işleminden önce filtreleme, bölütleme ve normalizasyon gibi ön işleme teknikleri uygulanmıştır. Yapılan deneysel çalışmalarda %98,08’lik doğru tanıma oranına ulaşılmıştır.Article Citation - WoS: 4Citation - Scopus: 4Modeling and Simulation of Positron Emission Mammography (pem) Based on Double-Sided Cdte Strip Detectors(IOP Publishing Ltd., 2014) Özşahin, İlker; Ünlü, Mehmet ZübeyirBreast cancer is the most common leading cause of cancer death among women. Positron Emission Tomography (PET) Mammography, also known as Positron Emission Mammography (PEM), is a method for imaging primary breast cancer. Over the past few years, PEMs based on scintillation crystals dramatically increased their importance in diagnosis and treatment of early stage breast cancer. However, these detectors have significant limitations like poor energy resolution resulting with false-negative result (missed cancer), and false-positive result which leads to suspecting cancer and suggests an unnecessary biopsy. In this work, a PEM scanner based on CdTe strip detectors is simulated via the Monte Carlo method and evaluated in terms of its spatial resolution, sensitivity, and image quality. The spatial resolution is found to be ∼ 1 mm in all three directions. The results also show that CdTe strip detectors based PEM scanner can produce high resolution images for early diagnosis of breast cancer.Article Citation - WoS: 45Citation - Scopus: 47A Comparative Performance Evaluation of Various Approaches for Liver Segmentation From Spir Images(Türkiye Klinikleri Journal of Medical Sciences, 2015) Göçeri, Evgin; Ünlü, Mehmet Zübeyir; Dicle, OğuzDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of seven different pseudo-3D liver segmentation approaches chosen from deterministic (K-means-based), probabilistic (Gaussian model-based), supervised neural network (multilayer perceptron-based), and deformable model-based (level set) segmentation methods. The results of quantitative and qualitative analyses using sensitivity, specificity, and accuracy metrics show that the multilayer perceptron-based approach and a level set-based approach, both of which use distance regularization terms and signed pressure force function, are the most successful methods for liver segmentation from spectral presaturation inversion recovery (SPIR) images. However, the multilayer perceptron-based segmentation method has a higher computational cost. The automatic method using the distance regularized level set evolution with signed pressure force function avoids the sensitivity of a user-defined initial contour for each slice, gives the most efficient results for liver segmentation after the preprocessing steps, and also requires less computational time.Conference Object Citation - WoS: 42Citation - Scopus: 43An Automatic Level Set Based Liver Segmentation From Mri Data Sets(Institute of Electrical and Electronics Engineers Inc., 2012) Göçeri, Evgin; Ünlü, Mehmet Zübeyir; Güzeliş, Cüneyt; Dicle, OğuzA fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results. © 2012 IEEE.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.