Back to articles
Articles
Volume: 27 | Article ID: art00053
Image
Deep Learning for Dental Hyperspectral Image Analysis
  DOI :  10.2352/issn.2169-2629.2019.27.53  Published OnlineOctober 2019
Abstract

The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.

Subject Areas :
Views 113
Downloads 30
 articleview.views 113
 articleview.downloads 30
  Cite this article 

Oleksandr Boiko, Joni Hyttinen, Pauli Fält, Heli Jäsberg, Arash Mirhashemi, Arja Kullaa, Markku Hauta-Kasari, "Deep Learning for Dental Hyperspectral Image Analysisin Proc. IS&T 27th Color and Imaging Conf.,  2019,  pp 295 - 299,  https://doi.org/10.2352/issn.2169-2629.2019.27.53

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2019
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society for Imaging Science and Technology