Pregled bibliografske jedinice broj: 1001331
Dental age estimation from panoramic X-ray images using statistical models
Dental age estimation from panoramic X-ray images using statistical models // Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis / Kovačič, Stanislav ; Lončarić, Sven ; Kristan, Matej ; Štruc, Vitomir ; Vučić, Mladen (ur.).
Ljubljana: Institute of Electrical and Electronics Engineers (IEEE), 2017. str. 25-30 doi:10.1109/ISPA.2017.8073563 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Dental age estimation from panoramic X-ray images
using statistical models
Autori
Čular, Luka ; Tomaić, Mia ; Subašić, Marko ; Šarić, Tea ; Sajković, Viktorija ; Vodanović, Marin
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis
/ Kovačič, Stanislav ; Lončarić, Sven ; Kristan, Matej ; Štruc, Vitomir ; Vučić, Mladen - Ljubljana : Institute of Electrical and Electronics Engineers (IEEE), 2017, 25-30
ISBN
978-1-5090-4011-7
Skup
International Symposium on Image and Signal Processing and Analysis
Mjesto i datum
Ljubljana, Slovenija, 18.09.2017. - 20.09.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
active shape model ; active appearance model ; dental X-ray images ; age estimation
Sažetak
This paper presents an application of computer vision methods to dental age estimation based on the lower third right molar in panoramic X-ray images. For this purpose, two statistical computer vision models are adjusted and applied: Active Shape Model and Active Appearance Model. Both models use shape and appearance of the object to find the outer contour, with the only difference being in the way appearance is used. Statistical models are used to extract features describing the selected tooth, and neural network is used to provide dental age estimation using the features as input. Our own dataset was created, consisting of panoramic X-ray images with known age. A manual segmentation of the selected tooth has been performed for each image in the training set, and the obtained outer contours were used to train both models. Promising preliminary results are presented.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Dentalna medicina
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Stomatološki fakultet, Zagreb