Application of image processing techniques to aid in the detection of vertical root fractures in digital periapical radiography.

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.