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    Automatic Landmark Detection through Circular Hough Transform in Cephalometric X-rays
    (Ieee, 2017) Duman, Elvan; Kokver, Yunus; Unver, Halil Murat; Erdem, Osman Ayhan
    In this paper, a knowledge based framework is proposed to detect automatically cephalometric landmarks: Porion (Po), Sella (S), Menton (Me), Pogonion (Pg) and Gnathion (Gn). In this way anomalies can be diagnosed easily by orthodontists. Our framework comprise of two main steps: (1) Adaptive Histogram Equalisation (AHE) is applied to clarify the image which is used to determine the method of treatment in orthodontics and obtained from the plain X-ray. (2) Circular Hough Transform method is used to locate the cephalometric landmarks automatically on the processed image, the method was tested on 7 cephalometric images and our framework accurately and automatically locates these 5 cephalometric landmarks.
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    Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
    (Mdpi, 2019) Unver, Halil Murat; Kokver, Yunus; Duman, Elvan; Erdem, Osman Ayhan
    Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively.