Segmentation and Classification of Dental Caries in Cone Beam Tomography Images Using Machine Learning and Image Processing

Luiz Guilherme Kasputis Zanini, Izabel Regina Rubira-Bullen, Fátima de Lourdes dos Santos Nunes

2024

Abstract

Dental caries are caused by bacterial action that demineralizes tooth enamel and dentin. It is a serious threat to oral health and potentially leads to inflammation and tooth loss if not adequately treated. Cone Beam Computed Tomography (CBCT), a three-dimensional (3D) imaging technique used in dental diagnosis and surgical planning, can potentially contribute to detection of caries. This study aims at developing a computational method to segment and classify caries in CBCT images. The process involves data preparation, segmentation of caries regions, extraction of relevant features, feature selection, and training machine learning algorithms. We evaluated our method performance considering different stages of caries severity based on the International Caries Detection and Assessment System scale. The best results were achieved using a Gaussian filter with a multimodal threshold with a convex hull for the region of interest segmentation, feature selection via Random Forest, and classification using a model based on k-nearest neighbors algorithm. We achieved outcomes with an accuracy of 86.20%, a F1-score of 86.18%, and a sensitivity of 83.35% in multiclass classification. These results show that our approach contributes to the early segmentation and classification of dental caries, thereby improving oral health outcomes and treatment planning.

Paper Citation

in Harvard Style

Kasputis Zanini L., Rubira-Bullen I. and dos Santos Nunes F. (2024). Segmentation and Classification of Dental Caries in Cone Beam Tomography Images Using Machine Learning and Image Processing. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 428-435. DOI: 10.5220/0012365300003657

in Bibtex Style

@conference{healthinf24,
author={Luiz Guilherme Kasputis Zanini and Izabel Regina Rubira-Bullen and Fátima de Lourdes dos Santos Nunes},
title={Segmentation and Classification of Dental Caries in Cone Beam Tomography Images Using Machine Learning and Image Processing},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012365300003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Segmentation and Classification of Dental Caries in Cone Beam Tomography Images Using Machine Learning and Image Processing
SN - 978-989-758-688-0
AU - Kasputis Zanini L.
AU - Rubira-Bullen I.
AU - dos Santos Nunes F.
PY - 2024
SP - 428
EP - 435
DO - 10.5220/0012365300003657
PB - SciTePress