Aim/Purpose The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomograMapping Dental Restorations in Panoramic Radiographs 222 phy (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations 92%. Impact on Society Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.
[1]
Musbah J. Aqel,et al.
An Efficient Segmentation Algorithm for Panoramic Dental Images
,
2015
.
[2]
Robert Wanat,et al.
A Problem of Automatic Segmentation of Digital Dental Panoramic X-Ray Images for Forensic Human Identification
,
2011
.
[3]
Reinhard Klette,et al.
Panoramic Imaging: Sensor-Line Cameras and Laser Range-Finders
,
2008
.
[4]
Gilson Antonio Giraldi,et al.
Dental R-Ray Image Segmentation Using Texture Recognition
,
2014,
IEEE Latin America Transactions.
[5]
S. Perschbacher.
Interpretation of panoramic radiographs.
,
2012,
Australian dental journal.
[6]
A Ruprecht,et al.
Parameters of radiologic care: An official report of the American Academy of Oral and Maxillofacial Radiology.
,
2001,
Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.
[7]
J. Ludlow,et al.
Patient risk related to common dental radiographic examinations: the impact of 2007 International Commission on Radiological Protection recommendations regarding dose calculation.
,
2008,
Journal of the American Dental Association.
[8]
O. Vassend.
Anxiety, pain and discomfort associated with dental treatment.
,
1993,
Behaviour research and therapy.
[9]
Chanjira Sinthanayothin,et al.
Wavelet transformation for dental X-ray radiographs segmentation technique
,
2010,
2010 Eighth International Conference on ICT and Knowledge Engineering.
[10]
Debora Delai,et al.
Florid Cemento-osseous Dysplasia: A Case of Misdiagnosis.
,
2015,
Journal of endodontics.
[11]
Hugo Proença,et al.
Caries Detection in Panoramic Dental X-ray Images
,
2011
.
[12]
Theekapun Charoenpong,et al.
Teeth segmentation from dental x-ray image by template matching
,
2016,
2016 9th Biomedical Engineering International Conference (BMEiCON).
[13]
Om P Kharbanda,et al.
Comparison of radiation levels from computed tomography and conventional dental radiographs
,
2003,
Australian orthodontic journal.
[14]
S. Guttenberg.
Oral and maxillofacial pathology in three dimensions.
,
2008,
Dental clinics of North America.