Lucas Exposto Soares1, Deborah Queiroz Freitas2, Kaique Leite de Lima3, Lorena Rosa Silva3, Fernanda Paula Yamamoto-Silva3, Marcelo Andrade da Costa Vieira4. 1. Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trab. São Carlense, 400, São Carlos, São Paulo, 13566-590, Brazil. 2. Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Av. Limeira, 901, Piracicaba, São Paulo, 13414-903, Brazil. 3. Department of Stomatological Sciences, School of Dentistry, Federal University of Goiás, Av. Universitária s/n, Goiânia, Goiás, 74605-220, Brazil. 4. Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trab. São Carlense, 400, São Carlos, São Paulo, 13566-590, Brazil. mvieira@sc.usp.br.
Abstract
OBJECTIVES: To present an image processing framework to improve the detection of vertical root fractures (VRFs) in digital periapical radiography. MATERIALS AND METHODS: Thirty endodontically treated human teeth (15 of them fractured with a metal post inserted into them, and 15 for the control) were enclosed in a dry mandible and radiographed individually. The proposed framework was applied to the raw data, as a preprocessing step, and was composed of four stages: geometric adjustment and negative, denoising, adaptive contrast enhancement, and gamma correction. The contrast-to-noise ratio (CNR) and sharpness of the image's VRF region were used for the objective evaluation of the method. In addition, five examiners evaluated the original and enhanced images, using a 5-point scale to assess confidence. RESULTS: The objective results showed that the proposed framework increased the CNR of the VRF region by 173% compared to the standard preprocessing method provided by the detector's manufacturer. The results found by the human observers indicated that the area under the curve (AUC) and sensitivity of the diagnosis of VRF significantly increased by 4% and 17% (p ≤ 0.05), respectively, when the examiners evaluated the image with the proposed method concomitantly with the image available in the commercial software. However, the specificity was reduced. CONCLUSIONS: The proposed image processing framework can be used as an additional tool to that provided by the manufacturer to increase the sensitivity and AUC of the diagnosis of VRF. CLINICAL RELEVANCE: The proposed method can be easily used in clinical practice to aid VRF detection, since it does not incur high computational costs and does not increase the radiation dose applied to the patient.
OBJECTIVES: To present an image processing framework to improve the detection of vertical root fractures (VRFs) in digital periapical radiography. MATERIALS AND METHODS: Thirty endodontically treated human teeth (15 of them fractured with a metal post inserted into them, and 15 for the control) were enclosed in a dry mandible and radiographed individually. The proposed framework was applied to the raw data, as a preprocessing step, and was composed of four stages: geometric adjustment and negative, denoising, adaptive contrast enhancement, and gamma correction. The contrast-to-noise ratio (CNR) and sharpness of the image's VRF region were used for the objective evaluation of the method. In addition, five examiners evaluated the original and enhanced images, using a 5-point scale to assess confidence. RESULTS: The objective results showed that the proposed framework increased the CNR of the VRF region by 173% compared to the standard preprocessing method provided by the detector's manufacturer. The results found by the human observers indicated that the area under the curve (AUC) and sensitivity of the diagnosis of VRF significantly increased by 4% and 17% (p ≤ 0.05), respectively, when the examiners evaluated the image with the proposed method concomitantly with the image available in the commercial software. However, the specificity was reduced. CONCLUSIONS: The proposed image processing framework can be used as an additional tool to that provided by the manufacturer to increase the sensitivity and AUC of the diagnosis of VRF. CLINICAL RELEVANCE: The proposed method can be easily used in clinical practice to aid VRF detection, since it does not incur high computational costs and does not increase the radiation dose applied to the patient.
Authors: M G O Pinto; K A Rabelo; S L Sousa Melo; P S F Campos; L S A F Oliveira; P M Bento; D P Melo Journal: Int Endod J Date: 2016-05-23 Impact factor: 5.264
Authors: Nicolly Oliveira-Santos; Hugo Gaêta-Araujo; Débora Costa Ruiz; Eduarda Helena Leandro Nascimento; Wilson Gustavo Cral; Christiano Oliveira-Santos; Francisco Carlos Groppo Journal: Clin Oral Investig Date: 2022-03-10 Impact factor: 3.606