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Öğe Comparison of Trajectories Formed With Kinect Motion Capture Data(Ieee, 2015) Sen, Mehmed Oguz; Erturkler, MetinMotion capture is the process of recording people or object movements. Generally, marker based systems are used for this process. In these systems, motion data is acquired by placing markers on certain points on the object whose motion is going to be captured. However preserving markers' positions on the object, dependency on the object, high cost and requirement of specially designed workspace causes difficulties in usage. In recent years, development of motion sensing camera systems like Microsoft Kinect became an alternative of marker based systems. In this study, Frechet distance algorithm is suggested for similarity measurement of movement trajectories that are formed by motion capture data acquired using Microsoft Kinect cameras. Measurement of movement similarity is crucial for certain applications. (activity recognition, object classification, path finding for humanoid robots etc.)Öğe K-Nearest Unrepeatable Cell Graph Model of Histopathological Tissue Image(Ieee, 2015) Serin, Faruk; Erturkler, Metin; Gul, MehmetOne of the most important components in the histopathological tissue images is the cell nuclei. Features such as the number, morphological properties and location of the cell nuclei offer useful information for histopathological analysis. Cell-graph models are constructed using location information of cell nuclei and important distinctive information can be obtained from the features of the models. The models are generally formed according to the distance between the cell nuclei. However, the distance between the cell nuclei is affected by various factors during obtaining tissue image and shows variety. In this study, using one-way neighborhood relationship of the nuclei with each other is proposed for the construction of the cell-graph models of histopathological images. The proposed approach has been tested on 20 healthy and 20 necrotic liver tissue images. The results show that graph models constructed by the neighborhood relationship, have more distinctive characteristics than distance-based graph models.Öğe A novel overlapped nuclei splitting algorithm for histopathological images(Elsevier Ireland Ltd, 2017) Serin, Faruk; Erturkler, Metin; Gul, MehmetBackground and objective: Nuclei segmentation is a common process for quantitative analysis of histopathological images. However, this process generally results in overlapping of nuclei due to the nature of images, the sample preparation and staining, and image acquisition processes as well as insufficiency of 2D histopathological images to represent 3D characteristics of tissues. We present a novel algorithm to split overlapped nuclei. Methods: The histopathological images are initially segmented by K-Means segmentation algorithm. Then, nuclei cluster are converted to binary image. The overlapping is detected by applying threshold area value to nuclei in the binary image. The splitting algorithm is applied to the overlapped nuclei. In first stage of splitting, circles are drawn on overlapped nuclei. The radius of the circles is calculated by using circle area formula, and each pixel's coordinates of overlapped nuclei are selected as center coordinates for each circle. The pixels in the circle that contains maximum number of intersected pixels in both the circle and the overlapped nuclei are removed from the overlapped nuclei, and the filled circle labeled as a nucleus. Results: The algorithm has been tested on histopathological images of healthy and damaged kidney tissues and compared with the results provided by an expert and three related studies. The results demonstrated that the proposed splitting algorithm can segment the overlapping nuclei with accuracy of 84%. Conclusions: The study presents a novel algorithm splitting the overlapped nuclei in histopathological images and provides more accurate cell counting in histopathological analysis. Furthermore, the proposed splitting algorithm has the potential to be used in different fields to split any overlapped circular patterns. (C) 2017 Elsevier B.V. All rights reserved.Öğe A Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Images(Gazi Univ, 2020) Serin, Faruk; Erturkler, Metin; Gul, MehmetIn this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&E stained histopathological tissue images. The proposed algorithm does not require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels' color value in the images with RGB color space, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. Two more algorithms are also proposed to distinguish nuclei cluster from the other clusters obtained by K-means, and eliminate false positives in nuclei cluster, which are not nuclei. In order to compare and evaluate the performance of the proposed segmentation algorithm in terms of time and cost efficiency, K -Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations show that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K -Means and expert is 5%.Öğe A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing(Wiley, 2020) Serin, Faruk; Erturkler, MetinThe cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell-location-based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance-based threshold, K-Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell-graph. Histopathological images of liver were classified by using features extracted from T-Distance, K-Nearest Neighbor, Voronoi, Delaunay, and the proposed cell-graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell-graph model.Öğe A theoretical investigation on moving average filtering solution for fixed-path map matching of noisy position data(Inderscience Enterprises Ltd, 2019) Alagoz, Baris Baykant; Erturkler, Metin; Yeroglu, CelaleddinPrecisely estimation of moving object locations from position sensors promises useful implications for many fields of engineering. The mapping of a moving object on a predefined path is an important process for object tracking and remote control applications. Owing to measurement noises of sensors and uncertainties, the measured object location may not precisely match to paths or roads in a map. This study presents a numerical method for a low computational-complexity solution of point to arc type mapping problems. This method has two main tasks: a noise reduction task by short-time moving average filtering of noisy two-dimensional position data, and a map matching task to estimate exact position of an object on a map. To evaluate performance of the investigated method, the algorithm is applied for bus route tracking simulations and results are discussed for several road scenarios and various levels of noise.
