# Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
## 3 Results  
A schematic of the tooth and normal periodontitis showing the V-shaped identification of the CEJ was illustrated in Figure 4a. The ultrasound images before and after image preprocessing are shown in Figure 4b,c. The homomorphic filtering with a Gaussian filter removed multiplicative noise significantly and sharpened features of the image. By  
Figure 3: An example shows three different bounding box sizes: SBB in yellow, MBB in green, and LBB in red in the same image. enhancing contrast, the structures of enamel and cement become much sharper and are clearly distinguishable from other regions. Adaptive median filtering suppressed subtle speckle noise while preserving edge detail. Compared to the original image, the preprocessing improved the clarity of the images, making the following extraction more reliable and tenable. The average computational time for the preprocessing steps was 0.09 s for each image. Figure 5 illustrates the segmentation and CEJ detection by the proposed method. The algorithm initially required a bounding box (81 \(\times\) 101 pixels in this example) containing the CEJ (Figure 5a). Then, the image was classified into several areas based on the gray-level values. Figure 5b presents a color plot of the ROI where the high-intensity areas correspond to the enamel, cementum, or bone structures. Finally, _K_-means clustering accurately extracted the two clusters of interest while rejecting other areas, resulting in a binary map where the hard tissues (enamel or bone) are indicated in black (Figure 5c). It took approximately 0.69 s for the segmentation on average. Figure 5d shows a curve joining the upper borders of the identified pixels of the extracted region. The numerical differential between adjacent pixels along the curve was estimated (Figure 5e), and the CEJ was determined at the position of the maximum absolute of the differential (Figure 5f). The average time of the CEJ detection process was 0.27 s. The total time for the CEJ prediction including preprocessing steps was 1.05 s. The identified position was then incorporated into the image as a red dot (Figure 5g). Figure 4: (**a**) A schematic cross-section of a tooth and its periodontitis; (**b**) the in vivo intraoral ultrasound images before image processing; (**c**) the processed image after homomorphic filtering, contrast enhancement, and adaptive median filtering. Table 1 summarizes the CEJ detection results between two manual labels by the same rater (intrarater), between two raters (interrater), and the difference between raters and the method (intermethod) with and without image preprocessing and for three bounding box sizes, i.e., small (SBB), medium (MBB), large (LBB). The two scenarios are with all three image preprocessing steps (A or proposed method) and without image preprocessing steps (B). The clinical tolerance limit of 0.5 mm based on direct probing measurements was used as a reference [33]. The MDs \(\pm\) SDs of the mismatches vary from 0.21 \(\pm\) 0.24 mm to 0.31 \(\pm\) 0.32 mm for the intrarater and around 0.23 \(\pm\) 0.24 mm for the interrater. The average ICC values and percentages of images that have less than 0.5 mm mismatch between the  
Figure 5: An example of the computer-assisted CEJ detection on an ultrasound image. (**a**) A bounding box containing the CEJ; (**b**) the corresponding color image of the ROI; (**c**) region extraction using \(K\)-mean clustering; (**d**) edge detection; (**e**) local difference calculation; (**f**) CEJ identification at maximum difference; (**g**) comparison of the CEJ location by the proposed method (red dot) and manual CEJ identification by raters 1 and 2 (green and yellow dots). proposed method and two raters were 0.990 and 96%; 0.985 and 92%; and 0.982 and 90% for SBB, MBB, and LBB, respectively. These values were close to the interrater (93%) and higher than the intrarater average (88%).