Dental segmentation in cone-beam computed tomography images using watershed and morphology operators

Abstract

Teeth segmentation is an important task in computer-aided procedures and clinical diagnosis. In this paper, we propose an accurate and robust algorithm based on watershed and morphology operators for teeth and pulp segmentation and a new approach for enamel segmentation in cone-beam computed tomography (CBCT) images. Proposed method consists of five steps: acquiring appropriate CBCT image, image enhancement, teeth segmentation using the marker-controlled watershed (MCW), enamel segmentation by global threshold, and finally, utilizing the MCW for pulp segmentation. Proposed algorithms evaluated on a dataset consisting 69 patient images. Experimental results show a high accuracy and specificity for teeth, enamel, and pulp segmentation. MCW algorithm and local threshold are accurate and robust approaches to segment tooth, enamel, and pulp tissues. Methods overcome the over-segmentation phenomenon and artifacts reduction.

Semi or fully automatic tooth segmentation in CBCT images: a review

“…Compared to edge-based methods, these approaches place greater emphasis on the overall contextual information within the segmented regions, enabling them to adapt to gradual variations or blurred tooth boundaries. To validate the performance of these methods in different patients, several scholars ( Kang et al, 2015 ; Jiang et al, 2019 ; Kakehbaraei, Seyedarabi & Zenouz, 2018 ) started collecting clinical CBCT images (7–10 patients) and conducted quantitative statistical analyses on the segmentation results.…”

“…To compensate for the shortcomings of existing software products for low-contrast CBCT images, Naumovich, Naumovich & Goncharenko (2015) developed software and algorithms based on watershed transformation. The method proposed by Kakehbaraei, Seyedarabi & Zenouz (2018) utilizes the marker-controlled watershed (MCW) algorithm and achieves outstanding results in terms of sensitivity (0.9414), specificity (0.9994), and accuracy (0.9993). Galibourg et al (2018) employed a watershed-based method to conduct automatic tooth segmentation via CBCT and compared its performance with that of a validated semiautomatic segmentation method.…”

“…Segmenting extremely small teeth ( Ma & Yang, 2019 ), irregularly arranged teeth ( Gan et al, 2017 ), and impacted third molars ( Qian et al, 2021 ) with diverse eruption positions and shapes ( Wu et al, 2020 ) is challenging. Additionally, the proximity of adjacent teeth ( Mortaheb, Rezaeian & Soltanian-Zadeh, 2013 ; Kakehbaraei, Seyedarabi & Zenouz, 2018 ; Kakehbaraei, Seyedarabi & Zenouz, 2018 ; Ma & Yang, 2019 ) or closely positioned teeth during occlusion ( Gan et al, 2017 ; Kakehbaraei, Seyedarabi & Zenouz, 2018 ; Qian et al, 2021 ; Wu et al, 2020 ) may induce mutual interference, leading to segmentation failure in delineating joint contours.…”

“…Several quality parameters of CBCT images, such as low resolution, noise, contrast deficiency ( Galibourg et al, 2018 ; Mortaheb, Rezaeian & Soltanian-Zadeh, 2013 ), and the presence of metal artifacts ( Kang et al, 2015 ; Gan et al, 2017 ; Kakehbaraei, Seyedarabi & Zenouz, 2018 ), negatively impact segmentation outcomes. In response to these constraints, segmentation pipelines undergo continual refinement.…”

A Novel Collaborative Learning Model for Teeth and Fillings in Radiographs

“…Their model employed a deep neural network to obtain the binary mask of the teeth to statistically identify form and space changes and thereby improve the segmentation quality. A new method was pioneered for segmenting teeth, including tooth compartments (pulp, enamel), by combining a marker-controlled watershed (MCW) algorithm with local threshold techniques to assess CBCT images (Kakehbaraei et al 2018). Zhang et al proposed a model that used a deep CNN for accurate and autonomous segmentation, with an average accuracy of 98.8%, which was considered to be suitable for dental computer-aided design (CAD) systems (Zhang et al 2020).…”