Browsing by Author "Karacor, Deniz"

Now showing 1 - 6 of 6

Citation - WoS: 122
Citation - Scopus: 157
Automatic Segmentation, Counting, Size Determination and Classification of White Blood Cells
(Elsevier Sci Ltd, 2014) Nazlibilek, Sedat; Karacor, Deniz; Ercan, Tuncay; Sazli, Murat Husnu; Kalender, Osman; Ege, Yavuz; Department of Mechatronics Engineering; 01. Atılım University
The counts, the so-called differential counts, and sizes of different types of white blood cells provide invaluable information to evaluate a wide range of important hematic pathologies from infections to leukemia. Today, the diagnosis of diseases can still be achieved mainly by manual techniques. However, this traditional method is very tedious and time-consuming. The accuracy of it depends on the operator's expertise. There are laser based cytometers used in laboratories. These advanced devices are costly and requires accurate hardware calibration. They also use actual blood samples. Thus there is always a need for a cost effective and robust automated system. The proposed system in this paper automatically counts the white blood cells, determine their sizes accurately and classifies them into five types such as basophil, lymphocyte, neutrophil, monocyte and eosinophil. The aim of the system is to help for diagnosing diseases. In our work, a new and completely automatic counting, segmentation and classification process is developed. The outputs of the system are the number of white blood cells, their sizes and types. (C) 2014 Elsevier Ltd. All rights reserved.
Citation - WoS: 23
Citation - Scopus: 30
Discrete Lissajous Figures and Applications
(Ieee-inst Electrical Electronics Engineers inc, 2014) Karacor, Deniz; Nazlibilek, Sedat; Sazli, Murat H.; Akarsu, Eyup S.; Department of Mechatronics Engineering; 01. Atılım University
In this paper, an innovative method based on an algorithm utilizing discrete convolutions of discrete-time functions is developed to obtain and represent discrete Lissajous and recton functions. They are actually discrete auto- and cross-correlation functions. The theory of discrete Lissajous figures is developed. The concept of rectons is introduced. The relation between the discrete Lissajous figures and autocorrelation functions is set. Some applications are described including phase, frequency, and period determination of periodic signals, time-domain characteristics (such as damping ratio) of a control system, and abnormality and spike detection within a signal, are described. In addition, an electrocardiogram signal with an abnormality of atrial fibrillation is given for abnormality detection by means of recton functions. An epileptic activity detection within an electroencephalography signal is also given.
Citation - WoS: 19
Citation - Scopus: 19
Identification of Materials With Magnetic Characteristics by Neural Networks
(Elsevier Sci Ltd, 2012) Nazlibilek, Sedat; Ege, Yavuz; Kalender, Osman; Sensoy, Mehmet Gokhan; Karacor, Deniz; Sazh, Murat Husnu; Department of Mechatronics Engineering; 01. Atılım University
In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 12
A Magnetic Measurement System and Identification Method for Buried Magnetic Materials Within Wet and Dry Soils
(Ieee-inst Electrical Electronics Engineers inc, 2016) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Erturk, Korhan Levent; Karacor, Deniz; Information Systems Engineering; Department of Mechatronics Engineering; 06. School Of Engineering; 01. Atılım University
In this paper, a new magnetic measurement system is developed to determine upper surfaces of buried magnetic materials, particularly land mines. This measurement system uses the magnetic-anomaly-detection method. It also has intelligent identification software based on an image matching algorithm. It is aimed to determine and identify the buried ferromagnetic materials with minimum energy consumption. It is concentrated on the detection and identification of the shapes of upper surfaces of buried magnetic materials in dry and wet conditions. The effect of humidity in the detection process for detection is tested. In this paper, we used sensor images to identify various ferromagnetic materials and similar objects. Sensor images of soils at various humidities covering the objects were obtained. We used the speeded-up-feature-transform algorithm in the comparison process of the images. Dry soil sample images match with the corresponding wet soil samples with the highest matching rate. The images for different objects can easily be distinguished by the matching process.
Citation - WoS: 5
Citation - Scopus: 5
A Study on the Performance of Magnetic Material Identification System by Sift-Brisk and Neural Network Methods
(Ieee-inst Electrical Electronics Engineers inc, 2015) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, Gokhan; Computer Engineering; Department of Mechatronics Engineering; 06. School Of Engineering; 01. Atılım University
Industry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.
Citation - WoS: 8
Citation - Scopus: 13
White Blood Cells Classifications by SURF Image Matching, PCA and Dendrogram
(Allied Acad, 2015) Nazlibilek, Sedat; Karacor, Deniz; Erturk, Korhan Levent; Sengul, Gokhan; Ercan, Tuncay; Aliew, Fuad; Department of Mechatronics Engineering; Information Systems Engineering; Computer Engineering; Department of Mechatronics Engineering; Information Systems Engineering; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Determination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils, lymphocytes, neutrophils, monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting, size determination and classification of white blood cells. In this paper, we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques.