Boosting Proximal Dental Caries Detection via Combination of Variational Methods and Convolutional Neural Network

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Proximal dental caries are diagnosed using dental X-ray images. Unfortunately, the diagnosis of proximal dental caries is often stifled due to the poor quality of dental X-ray images. Therefore, we propose an automatic detection system to detect proximal dental caries in periapical images for the first time. The system comprises four modules: horizontal alignment of pictured teeth, probability map generation, crown extraction, and refinement. We first align the pictured teeth horizontally as a pre-process to minimize performance degradation due to rotation. Next, a fully convolutional network are used to produce a caries probability map while crown regions are extracted based on optimization schemes and an edge-based level set method. In the refinement module, the caries probability map is refined by the distance probability modeled by crown regions since caries are located near tooth surfaces. Also we adopt non-maximum suppression to improve the detection performance. Experiments on various periapical images reveal that the proposed system using a convolutional neural network (CNN) and crown extraction is superior to the system using a naïve CNN.
Publisher
Springer US
Issue Date
2018-01
Language
English
Article Type
Article
Keywords

X-RAY IMAGES; HUMAN IDENTIFICATION; ACTIVE CONTOURS; SYSTEM; CLASSIFICATION; HISTOGRAMS; FEATURES; TEETH; MODEL

Citation

Journal of Signal Processing Systems, v.90, no.1, pp.87 - 97

ISSN
1939-8018
DOI
10.1007/s11265-016-1214-6
URI
http://hdl.handle.net/10203/240117
Appears in Collection
EE-Journal Papers(저널논문)
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