Yazar "Kocer, Hasan Erdinc" seçeneğine göre listele

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    Breast Cancer Classification with Wavelet Neural Network
    (IEEE, 2017) Ucar, Kursad; Kocer, Hasan Erdinc
    In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting them are presented. In addition, the classical neural network has been trained and tested. The results of these two learning algorithms are also presented in comparison.
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    COMPUTER AIDED CONTROL OF CUTTING ERROR IN TEXTILE PRODUCTS
    (EGE UNIV, 2017) Cevik, Kerim Kursat; Kocer, Hasan Erdinc
    At present, the audits about the cutting error of textile products (leather, fabric, etc.) are made by the human by the eye via the template. Making these audits that necessitate accurate measurement by eye both takes so much time and enhance the risk occurrence risk. In this article, the image processing based industrial quality control system that determines the cutting errors of textile products automatically and discriminates between faulty and faultless products is explained. The system minimizes the faults based upon the human auditing and increases the number of pieces that are controlled by the unit of time. The performed system is composed of Panel PC, line scan camera, system of conveyor, basket control unit, image processing software and control user interface. The textile pieces (cuts) to be inspected come into the part by the conveyor where the camera and illumination unit are available, and the image is captured. This captured image is sent to the Panel PC and controlled whether there is a cutting error via image processing software. According to the result of the audit, the basket system at the end of the conveyor (conveyor belt) moves back and forth on wheel rail, and the textile pieces are provided to fall into the required basket. The performed system was tested on the leather pieces that were taken from a company in the leather sector. Totally it was tried by 150 times for 50 pieces of leather in 5 different templates and these pieces felt into the required basket correctly by discriminating for faulty/faultiness ones by 149 times (99,33% success ratio).
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    Computer-assisted automatic egg fertility control
    (KAFKAS UNIV, VETERINER FAKULTESI DERGISI, 2019) Boga, Mustafa; Cevik, Kerim Kursat; Kocer, Hasan Erdinc; Burgut, Aykut
    This research aimed to determine the fertilization control of the eggs in an incubator between 0th and 5th days by image processing techniques via low-priced tools. Three different datasets that were composed of eggs whose images taken at different times in the incubator were prepared. Several filtering and morphology methods, gray level conversion and dynamic thresholding were utilized to process the 15 egg images. Moreover, the original processing codes based on the problem were given. White and Black percentages of binary images were utilized to determine the egg control. According to the test results, for the first dataset; 73.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy was achieved on the fourth day, for the second dataset; 93.34% of fertility accuracy was achieved on the third day; 93.34% of fertility accuracy was achieved again on the fourth day; for the third dataset, 93.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy again was achieved on the fourth day. When the results were evaluated, it was seen that egg fertility has been determined successfully automated with low cost tools.
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    Developmental Hip Dysplasia Segmentation of Ultrasound Images
    (IEEE, 2016) Cevik, Kerim Kursat; Kocer, Hasan Erdinc
    In our study, Developmental Dysplasia of the Hip (DDH) is intended to automatically segmenting the ultrasound images for diagnosis. Initially, a filter is applied to the raw images. Seven different filters (Mean, Median, Gaussian, Wiener, Perona & Malik, Lee and Frost) are applied to the images and finally the output images are evaluated. Filtered DDH images were segmented and results are evaluated in the second part of the work. In the DDH diagnosis, the ilium and femoral regions are segmented by using Active Contour Models and Circular Hough Transform methods, respectively. When the segmentation process is analyzed, it is observed that the Wiener filters manage to increase the success rate due to their ability to remove speckle noise and ilium segmentation was performed with 94%. It is observed that Wiener filter was also success, besides when applied histogram equalization after filtering success rate is determined as 96% in the femoral region.
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    Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip
    (KOWSAR PUBL, 2016) Kocer, Hasan Erdinc; Cevik, Kerim Kursat; Sivri, Mesut; Koplay, Mustafa
    Background: Developmental dysplasia of the hip(DDH) can be detected with ultrasonography (USG) images. However, the accuracy of this method is dependent on the skill of the radiologist. Radiologists measure the hip joint angles without computer-based diagnostic systems. This causes mistakes in the diagnosis of DDH. Objectives: In this study, we aimed to automate segmentation of DDH ultrasound images in order to make it convenient for radiologic diagnosis by this recommended system. Materials and Methods: This experiment consisted of several steps, in which pure DDH and various noise-added images were formed. Then, seven different filters (mean, median, Gaussian, Wiener, Perona and Malik, Lee, and Frost) were applied to the images, and the output images were evaluated. The study initially evaluated the filter implementations on the pure DDH images. Then, three different noise functions, speckle, salt and pepper, and Gaussian, were applied to the images and the noisy images were filtered. In the last part, the peak signal to noise ratio (PSNR) and mean square error (MSE) values of the filtered images were evaluated. PSNR and MSE distortion measurements were applied to determine the image qualities of the original image and the output image. As a result, the differences in the results of different noise removal filters were observed. Results: The best results of PSNR values obtained in filtering were: Wiener (43.49), Perona and Malik (27.68), median (40.60) and Lee (35.35) for the noise functions of raw images, Gaussian noise added, salt and pepper noise added and speckle noise added images, respectively. After the segmentation process, it was seen that applying filtering to DDH USG images had low influence. We correctly segmented the ilium zone with the active contour model. Conclusion: Various filters are needed to improve the image quality. In this study, seven different filters were implemented and investigated on both noisy and noise-free images.
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    Segmentation of the Ilium and Femur Regions from Ultrasound Images for Diagnosis of Developmental Dysplasia of the Hip
    (AMER SCIENTIFIC PUBLISHERS, 2016) Cevik, Kerim Kursat; Kocer, Hasan Erdinc; Andac, Seyda
    The objective of the study is to evaluate the efficiency of applying filters on ultrasound images in order to increase the success rate of segmentation in the diagnosis of Developmental Dysplasia of the Hip (DDH). This research consists of several steps, in which pure DDH images are formed. Seven different filters (Mean, Median, Gaussian, Wiener, Perona and Malik, Lee and Frost) are applied to the images and finally the output images are evaluated. Initially, a filter is applied to the raw images. To assess the resulting images peak signal to noise ratio (PSNR) and mean square error (MSE) values are used. In the next section of the study, those seven different filters are applied to the raw images and segmentation is carried out and then the results are evaluated. In the DDH diagnosis, the ilium and femoral regions are segmented by using Active Contour Models and Circular Hough Transform methods, respectively. The results of the study show that applying Wiener filter to the iliac region results in 100% success, while the filter also achieves 90% success rate in the femoral region. In conclusion, the examining PSNR and MSE values show that the degree of filter's success varies according to the type of noise contained in the image. When the segmentation process is analyzed, it is observed that the Wiener filters manage to increase the success rate due to their ability to remove speckle noise.