Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7443
Title: Segmentation of Teeth Region via Machine Learning in Panoramic X-Ray Dental Images
Authors: Güven, Ali
Yetik, İmam Şamil
Çulhaoğlu, Ahmet
Orhan, Kaan
Kılıçarslan, Mehmet Kılıçarslan
Keywords: dental panoramic X-Ray images
machine learning
image processing
image segmentation
teeth segmentation
Issue Date: 2020
Publisher: IEEE
Source: 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: Segmentation of teeth region from the dental panoramic X-Ray images is an important task in determining various diseases. The main goal of this article is to be able to automatically segment the region of teeth in panoramic x-ray images. First, the center point of the teeth area in the images was determined automatically. Then, a feature set was developed including intensity values of pixels, x-coordinate relative to this center point, y-coordinate relative to this point, and the pixel values obtained by subtraction of maximum and minimum values in 3x3 window. CatBoost algorithm was used for machine learning. When creating the machine learning model, k-fold cross validation of training data set and grid search optimization of hyper parameters, were applied to avoid over fitting of data set. The results were analyzed using the learning curve, F1, accuracy, recall, and precision scores.
URI: https://hdl.handle.net/20.500.11851/7443
ISBN: 978-1-7281-7206-4
ISSN: 2165-0608
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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