Yazar "Kocer, Hasan Erdinc" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim Yok
    Öğe
    Deep Learning Based Egg Fertility Detection
    (Mdpi, 2022) Cevik, Kerim Kursat; Kocer, Hasan Erdinc; Boga, Mustafa
    Simple Summary This study employs a Mask R-CNN technique along with the transfer learning model to accurately detect fertile and infertile eggs. It is a novel study that uses a single DL model to carry out detection, classification and segmentation of fertile and infertile eggs based on incubator images. This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs. Two different test processes were used in this study. In the first test application, a data set containing five fertile eggs was used. In the second, testing was carried out on the data set containing 18 fertile eggs. For evaluating this study, we used AP, one of the most important metrics for evaluating object detection and segmentation models in computer vision. When the results obtained were examined, the optimum threshold value (IoU) value was determined as 0.7. According to the IoU of 0.7, it was observed that all fertile eggs in the incubator were determined correctly on the third day of both test periods. Considering the methods used and the ease of the designed system, it can be said that a very successful system has been designed according to the studies in the literature. In order to increase the segmentation performance, it is necessary to carry out an experimental study to improve the camera and lighting setup prepared for taking the images.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.