# Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
## 4 Discussion  
Innovations in medical imaging have helped mankind to achieve an enhanced quality of life. Although X-rays have been the mainstay in medical imaging, the concern about the harmful effects of ionizing radiation, especially for pediatric and adolescent patients due to its frequent use in medicine and dentistry, is ever-growing. Current research has been focused on developing new modalities such as ultrasound and magnetic resonance for medical and dental imaging. Ultrasound uses compressional waves to image the internal structures of the tissues. The ultrasound technique has numerous benefits which include portability, cost savings, and especially safety for not using ionizing radiation. This imaging modality has the advantage of being able to measure the thickness and elasticity of the gingiva, which is deemed difficult with conventional radiographic imaging modalities. Beyond these advantages, ultrasound imaging has some drawbacks. The images lack resolution and contrast. The signal-to-noise ratio is low with speckle noise. Furthermore, interpreting the ultrasound images is challenging due to the interference of wave propagation characteristics. Increasing the frequency to enhance the resolution for fine details can be investigated, but the depth penetration becomes limited, and more ultrasound scattering is expected to deteriorate the image quality. CEJ is considered an important reference landmark for various clinical measurements in oral health assessment, such as gingival attachment level and alveolar bone crest level. Since CEJ is stable over time, it provides an excellent reference for evaluating disease progression and treatment intervention. The challenge of accurately localizing the CEJ is not only limited by errors in the measurement systems, but is compounded by the fact that the enamel and cementum boundaries are irregular, and their curvatures vary among different teeth. Based on the gradual thinning characteristics of the enamel toward the cementum, the ultrasound response shows a valley in the B-mode image with a low corresponding to the position of CEJ, and a detection method is proposed in this study to locate CEJ in ultrasound images. The proposed technique includes image processing steps prior to CEJ detection to minimize the multiplicative speckle noise and impulsive noise by homomorphic filtering \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **MD** & **SD** & **ICC** & **Images with Difference** \\  & & **(mm)** & **(mm)** & **ICC** & **\textless{} 0.5 mm (\%)** \\ \hline \multirow{3}{*}{Full size} & R11–R12 & 0.21 & 0.24 & 0.983 [0.978,0.987] & 90 \\ \cline{2-6}  & R21–R22 & 0.31 & 0.32 & 0.969 [0.960,0.978] & 85 \\ \cline{2-6}  & R1–R2 & 0.23 & 0.24 & 0.983 [0.979,0.987] & 93 \\ \hline \multirow{3}{*}{SBB \(31\times 41\) (pixel)} & R1–A & 0.23 & 0.12 & 0.991 [0.989,0.993] & 98 \\  & R2–A & 0.26 & 0.15 & 0.988 [0.984,0.991] & 94 \\ \cline{2-6}  & R1–B & 0.30 & 0.20 & 0.985 [0.980,0.988] & 87 \\  & R2–B & 0.33 & 0.19 & 0.980 [0.974,0.985] & 83 \\ \hline \multirow{3}{*}{MBB \(51\times 71\) (pixel)} & R1–A & 0.28 & 0.16 & 0.985 [0.981,0.989] & 92 \\  & R2–A & 0.30 & 0.17 & 0.984 [0.979,0.987] & 91 \\ \cline{2-6}  & R1–B & 0.46 & 0.33 & 0.959 [0.948,0.968] & 62 \\  & R2–B & 0.47 & 0.33 & 0.957 [0.945,0.967] & 63 \\ \hline \multirow{3}{*}{LBB \(81\times 101\) (pixel)} & R1–A & 0.29 & 0.18 & 0.983 [0.978,0.986] & 90 \\  & R2–A & 0.30 & 0.20 & 0.981 [0.976,0.985] & 89 \\ \cline{1-1} \cline{2-6}  & R1–B & 0.54 & 0.38 & 0.944 [0.928,0.956] & 57 \\ \cline{1-1} \cline{2-6}  & R2–B & 0.56 & 0.38 & 0.941 [0.924,0.954] & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CEJ identification between the proposed method and the two raters for different bounding box sizes (SBB, MBB, LBB) with image preprocessing scenarios (A) and without (B). The full size refers to the whole image size (e.g., 360 \(\times\) 386 pixels).