• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.

• Imaging Science in Dentistry
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• v.33 no.2
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• pp.71-77
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• 2003

Purpose : To evaluate and compare the efficacy of digital radiographic images in the detection of bone loss at the bifurcation area of the mandibular first molar with traditional film-based periapical radiographs, Materials and Methods : One dried human mandible with minimal periodontal bone loss around the first molar was selected and an artificial alveolar bone defect at the bifurcation area was serially prepared over 18 steps. Images were taken using a direct CCD-based system and with F-speed periapical films. The images were evaluated by seven interpreters (3 radiologists, 3 periodontologists, and 1 general dentist) using a 5-point confidence rating scale. Results : The readability of both periapical radiographs and digital image increased as the size of the artificial lesion and exposure time increased (p < 0.05). Periapical radiographs offered greater readability of smaller bone defects than digital images, and the coefficient of variation of mean score between periapical radiographs and digital images showed a significant difference. Conclusion : The experimental results indicate that a significant difference in the coefficient of variation of mean score exists between periapical radiographs and digital images, and that traditional film-based periapical images offer greater readability of smaller bone defects than digital images can presently offer.

• Journal of Periodontal and Implant Science
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• v.47 no.1
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• pp.13-19
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• 2017

Purpose: The purpose of this study was to examine whether periodontal pocket could be satisfactorily visualized by optical coherence tomography (OCT) and to suggest quantitative methods for measuring periodontal pocket depth. Methods: We acquired OCT images of periodontal pockets in a porcine model and determined the actual axial resolution for measuring the exact periodontal pocket depth using a calibration method. Quantitative measurements of periodontal pockets were performed by real axial resolution and compared with the results from manual periodontal probing. Results: The average periodontal pocket depth measured by OCT was $3.10{\pm}0.15mm$, $4.11{\pm}0.17mm$, $5.09{\pm}0.17mm$, and $6.05{\pm}0.21mm$ for each periodontal pocket model, respectively. These values were similar to those obtained by manual periodontal probing. Conclusions: OCT was able to visualize periodontal pockets and show attachment loss. By calculating the calibration factor to determine the accurate axial resolution, quantitative standards for measuring periodontal pocket depth can be established regardless of the position of periodontal pocket in the OCT image.

• Journal of Periodontal and Implant Science
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• v.47 no.1
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• pp.41-50
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• 2017

Purpose: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. Methods: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. Results: The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than $100{\mu}m$. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. Conclusions: We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.145-151
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• 2013

Purpose: This study was performed to evaluate and compare the radiopacity of dentin, enamel, and 8 restorative composites on conventional radiograph and digital images with different resolutions. Materials and Methods: Specimens were fabricated from 8 materials and human molars were longitudinally sectioned 1.0 mm thick to include both enamel and dentin. The specimens and tooth sections were imaged by conventional radiograph using #4 sized intraoral film and digital images were taken in high speed and high resolution modes using a phosphor storage plate. Densitometric evaluation of the enamel, dentin, restorative materials, a lead sheet, and an aluminum step wedge was performed on the radiographic images. For the evaluation, the Al equivalent (mm) for each material was calculated. The data were analyzed using one-way ANOVA and Tukey's test (p<0.05), considering the material factor and then the radiographic method factor, individually. Results: The high speed mode allowed the highest radiopacity, while the high resolution mode generated the lowest values. Furthermore, the high resolution mode was the most efficient method for radiographic differentiation between restorative composites and dentin. The conventional radiograph was the most effective in enabling differentiation between enamel and composites. The high speed mode was the least effective in enabling radiographic differentiation between the dental tissues and restorative composites. Conclusion: The high speed mode of digital imaging was not effective for differentiation between enamel and composites. This made it less effective than the high resolution mode and conventional radiographs. All of the composites evaluated showed radiopacity values that fit the ISO 4049 recommendations.

• Journal of Periodontal and Implant Science
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• v.51 no.2
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• pp.88-99
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• 2021

Purpose: Direct intraoral scanning and superimposing methods have recently been applied to measure the dimensions of periodontal tissues. The aim of this study was to analyze various correlations between labial gingival thickness and underlying alveolar bone thickness, as well as clinical parameters among 3 tooth types (central incisors, lateral incisors, and canines) using a digital method. Methods: In 20 periodontally healthy subjects, cone-beam computed tomography images and intraoral scanned files were obtained. Measurements of labial alveolar bone and gingival thickness at the central incisors, lateral incisors, and canines were performed at points 0-5 mm from the alveolar crest on the superimposed images. Clinical parameters including the crown width/crown length ratio, keratinized gingival width, gingival scallop, and transparency of the periodontal probe through the gingival sulcus were examined. Results: Gingival thickness at the alveolar crest level was positively correlated with the thickness of the alveolar bone plate (P<0.05). The central incisors revealed a strong correlation between labial alveolar bone thickness at 1 and 2 mm, respectively, inferior to the alveolar crest and the thickness of the gingiva at the alveolar crest line (G0), whereas G0 and labial bone thickness at every level were positively correlated in the lateral incisors and canines. No significant correlations were found between clinical parameters and hard or soft tissue thickness. Conclusions: Gingival thickness at the alveolar crest level revealed a positive correlation with labial alveolar bone thickness, although this correlation at identical depth levels was not significant. Gingival thickness, at or under the alveolar crest level, was not associated with the clinical parameters of the gingival features, such as the crown form, gingival scallop, or keratinized gingival width.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Imaging Science in Dentistry
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• v.32 no.2
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• pp.107-111
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• 2002

Purpose : To compare the accuracy of digital subtraction images acquired by two different methods different in positioning four reference points for geometrical standardization. Materials and Methods : A total of 36 digital radiographic images of 6 volunteers were taken at the areas of the incisor, premolar, and molar of both the maxilla and mandible using the Digora system. Each image was moved 4 mm vertically and horizontally. Four oral and maxillofacial radiologists performed digital subtraction radiography between the paired images before and after movement using Emago (Oral Diagnostic Systems, Amsterdam, The Netherlands) and Sunny (Biomedisys Co., Seoul, Korea). The standard deviation of the internal gray value in Region of Interest (ROI) was statistically analyzed between the two programs using the paired t-test. Results : The standard deviation of pixel gray values from the digital subtraction images using the Sunny program were lower than that of the Emago program (p<0.05). All observers showed significant differences between each other when the Sunny program was used (p<0.05), but one observer showed a significantly higher score than other observers when they used Emago (p<0.05). The standard deviations of premolar area from both Sunny and Emago programs were significantly higher than those of anterior and molar regions (p<0.05). Conclusion: The subtracted images using the Sunny program were more accurate and sensitive than those taken using the Emago program.

• Journal of Periodontal and Implant Science
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• v.46 no.6
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• pp.372-381
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• 2016

Purpose: The aim of this study was to determine the relationship between buccal bone thickness and gingival thickness by means of a noninvasive and relatively accurate digital registration method. Methods: In 20 periodontally healthy subjects, cone-beam computed tomographic images and intraoral scanned files were obtained. Measurements of buccal bone thickness and gingival thickness at the central incisors, lateral incisors, and canines were performed at points 0-5 mm from the alveolar crest on the superimposed images. The Friedman test was used to compare buccal bone and gingival thickness for each depth between the 3 tooth types. Spearman's correlation coefficient was calculated to assess the correlation between buccal bone thickness and gingival thickness. Results: Of the central incisors, 77% of all sites had a buccal thickness of 0.5-1.0 mm, and 23% had a thickness of 1.0-1.5 mm. Of the lateral incisors, 71% of sites demonstrated a buccal bone thickness <1.0 mm, as did 63% of the canine sites. For gingival thickness, the proportion of sites <1.0 mm was 88%, 82%, and 91% for the central incisors, lateral incisors, and canines, respectively. Significant differences were observed in gingival thickness at the alveolar crest level (G0) between the central incisors and canines (P=0.032) and between the central incisors and lateral incisors (P=0.013). At 1 mm inferior to the alveolar crest, a difference was found between the central incisors and canines (P=0.025). The lateral incisors and canines showed a significant difference for buccal bone thickness 5 mm under the alveolar crest (P=0.025). Conclusions: The gingiva and buccal bone of the anterior maxillary teeth were found to be relatively thin (<1 mm) overall. A tendency was found for gingival thickness to increase and bone thickness to decrease toward the root apex. Differences were found between teeth at some positions, although the correlation between buccal bone thickness and soft tissue thickness was generally not significant.