Intraoral radiographs texture analysis for dental implant planning

Mayara B V Mundim; Danilo R Dias; Ronaldo M Costa; Cláudio R Leles; Paulo M Azevedo-Marques; Rejane F Ribeiro-Rotta

doi:10.1016/j.cmpb.2016.08.012

Intraoral radiographs texture analysis for dental implant planning

Comput Methods Programs Biomed. 2016 Nov:136:89-96. doi: 10.1016/j.cmpb.2016.08.012. Epub 2016 Aug 24.

Authors

Mayara B V Mundim¹, Danilo R Dias¹, Ronaldo M Costa², Cláudio R Leles¹, Paulo M Azevedo-Marques³, Rejane F Ribeiro-Rotta⁴

Affiliations

¹ School of Dentistry, Universidade Federal de Goias, Avenida Universitária esquina com 1a Avenida s/n, Setor Universitário, 74605-220 Goiânia, Goiás, Brazil.
² Institute of Informatics, Universidade Federal de Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, 74690-900 Goiânia, Goiás, Brazil.
³ Department of Internal Medicine, Ribeirão Preto Medical School, Universidade de São Paulo, Avenida Bandeirantes, n° 3900, Bairro Monte Alegre, 14048-900, Ribeirao Preto, São Paulo, Brazil.
⁴ School of Dentistry, Universidade Federal de Goias, Avenida Universitária esquina com 1a Avenida s/n, Setor Universitário, 74605-220 Goiânia, Goiás, Brazil. Electronic address: rejanefrr@gmail.com.

PMID: 27686706
DOI: 10.1016/j.cmpb.2016.08.012

Abstract

Background and objectives: Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning.

Methods: Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement. From the superimposition of the pre- and post-implant images, four regions of interest (ROI) were delineated on the pre-implant images for each implant site: mesial, distal and apical peri-implant areas and a central area. Each ROI was analysed using Matlab® software and seven image attributes were extracted: mean grey level (MGL), standard deviation of grey levels (SDGL), coefficient of variation (CV), entropy (En), contrast, correlation (Cor) and angular second moment (ASM). Images were grouped by bone types-Lekholm and Zarb classification (1,2,3,4). Peak insertion torque (PIT) and resonance frequency analysis (RFA) were recorded during implant placement. Differences among groups were tested for each image attribute. Agreement between measurements of the peri-implant ROIs and overall ROI (peri-implant + central area) was tested, as well as the association between primary stability measures (PIT and RFA) and texture attributes.

Results: Differences among bone type groups were found for MGL (p = 0.035), SDGL (p = 0.024), CV (p < 0.001) and En (p < 0.001). The apical ROI showed a significant difference from the other regions for all attributes, except Cor. Concordance correlation coefficients were all almost perfect (ρ > 0.93), except for ASM (ρ = 0.62). Texture attributes were significantly associated with the implant stability measures.

Conclusion: Texture analysis of periapical radiographs may be a reliable non-invasive quantitative method for the assessment of jawbone and prediction of implant stability, with potential clinical applications.

Keywords: Computer-assisted; Computer-assisted diagnosis; Dental implants; Image processing; Implant stability; Texture analysis.

MeSH terms

Dental Implants*
Humans
Patient Care Planning*
Radiography, Dental*

Substances

Dental Implants