• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.383-387
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• 2015

Bowel and rectal diseases are on the increase by irregular life and westernized eating habits of modern people, especially rectal cancer, which accounts for 50% of the entire colon cancer. For the initial rectal cancer, because there is no portion projecting on the surface, if not see inside the tissue with ultrasound, you make an errors that misdiagnosis as rectal abscess. However there is a need for more accurate diagnosis, because it is sometimes difficult to distinguish abscess from rectal cancer depending on staging, in spite of the ultrasonic diagnosis. Therefore, this study was performed quantitative analysis by using a computer algorithm for rectal cancer and abscess image. Each of 20 cases about normal, abscess and cancer by setting analysis region ($50{\times}50$ pixels) applies to GLCM algorithm and Autocorrelation, Max probability, Sum average, Sum variance in each image were analyzed by comparing the 4 single parameter. Consequently, The high lesion detection efficiency was presented 100% by the 3 parameter of Autocorrelation, Max probability, Sum variance and the parameter of Sum average presents 95% in cancer, more than 90% in abscess. Those parameters are valuable in distinction standard about normal, cancer and abscess in rectum. It is sufficient availability as a computer assisted diagnosis system depended on clinical using.

• Imaging Science in Dentistry
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• v.47 no.1
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• pp.51-55
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• 2017

Purpose: This study was conducted to suggest the development of a learning management system for dental radiology education using the Modular Object-Oriented Dynamic Learning Environment(Moodle). Materials and Methods: Moodle is a well-known and verified open-source software-learning management system (OSS-LMS). The Moodle software was installed on a server computer and customized for dental radiology education. The system was implemented for teaching undergraduate students to diagnose dental caries in panoramic images. Questions were chosen that could assess students' diagnosis ability. Students were given several questions corresponding to each of 100 panoramic images. Results: The installation and customization of Moodle was feasible, cost-effective, and time-saving. By having students answer questions repeatedly, it was possible to train them to examine panoramic images sequentially and thoroughly. Conclusion: Based on its educational efficiency and efficacy, the adaptation of an OSS-LMS in dental school may be highly recommended. The system could be extended to continuing education for dentists. Further studies on the objective evaluation of knowledge acquisition and retention are needed.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.36-42
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• 2009

We have developed software for converting the volumetric voxel data obtained from X-ray computed tomography(CT) into computer-aided design(CAD) data. The developed software can used for non-destructive testing and evaluation, reverse engineering, and rapid prototyping, etc. The main algorithms employed in the software are image reconstruction, volume rendering, segmentation, and mesh data generation. The feasibility of the developed software is demonstrated with the CT data of human maxilla and mandible bones.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.227-237
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• 2003

Conventional studies of Computer Assisted Instruction(CAI) and Intelligent Tutoring System(ITS) have been general patterns to solve problems, so to solve specialized problems, the learner which has the attitude of passiveness should to solve problems including unnecessary processing to the need of the system. Consequently, those are not support the process of creativity and individual problems for the learner to solve the fractional number operations as this study. This study is the design and implementation of ITS on the fractional number addition and subtraction for the supplementary student. Our system can diagnosis mistakes of learning and guide the student to know their errors of learning process automatically And our system assist the learners to study with self-initiative learning, replacement their lacking of learning and control the process of fractional addition and subtraction operation with creativity according to their level. We showed that this system had improved problems of lacking care to supplementary student result in are not enough teachers involved their school and that the learner had achieved the higher learning effect according to the improved self-initiative learning causing this system.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• The Journal of Advanced Prosthodontics
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• v.15 no.6
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• pp.315-332
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• 2023

PURPOSE. This study aimed to investigate whether the accuracy of intraoral scanners is influenced by different scanning strategies in an in vitro setting, through a systematic review and meta-analysis. MATERIALS AND METHODS. This review was conducted in accordance with the PRISMA 2020 standard. The following PICOS approach was used: population, tooth impressions; intervention, the use of intraoral scanners with scanning strategies different from the manufacturer's instructions; control, the use of intraoral scanners following the manufacturers' requirements; outcome, accuracy of intraoral scanners; type of studies, in vitro. A comprehensive literature search was conducted across various databases including Embase, SciELO, PubMed, Scopus, and Web of Science. The inclusion criteria were based on in vitro studies that reported the accuracy of digital impressions using intraoral scanners. Analysis was performed using Review Manager software (version 5.3.5; Cochrane Collaboration, Copenhagen, Denmark). Global comparisons were made using a standardized mean difference based on random-effect models, with a significance level of α = 0.05. RESULTS. The meta-analysis included 15 articles. Digital impression accuracy significantly improved under dry conditions (P < 0.001). Moreover, trueness and precision were enhanced when artificial landmarks were used (P ≤ 0.02) and when an S-shaped pattern was followed (P ≤ 0.01). However, the type of light used did not have a significant impact on the accuracy of the digital intraoral scanners (P ≥ 0.16). CONCLUSION. The accuracy of digital intraoral scanners can be enhanced by employing scanning processes using artificial landmarks and digital impressions under dry conditions.

• Archives of Craniofacial Surgery
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• v.21 no.1
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• pp.64-68
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• 2020

The coexistence of craniofacial cleft and bilateral choanal atresia has only been reported in three cases in the literature, and only one of those cases involved a Tessier number 3 facial cleft. It is also rare for bilateral choanal atresia to be found in adulthood, with 10 previous cases reported in the literature. This report presents the case of a 19-year-old woman with a Tessier number 3 facial cleft who was diagnosed with bilateral choanal atresia in adulthood. At first, the diagnosis of bilateral choanal atresia was missed and septoplasty was performed. After septoplasty, the patient's symptoms did not improve, and an endoscopic examination revealed previously unnoticed bilateral choanal atresia. Computed tomography showed left membranous atresia and right bony atresia. The patient underwent an operation for opening and widening of the left choana with an image-guided navigation system (IGNS), which enabled accurate localization of the lesion while ensuring patient safety. Postoperatively, the patient became able to engage in nasal breathing and reported that it was easier for her to breathe, and there were no signs of restenosis at a 26-month follow-up. The patient was successfully treated with an IGNS.

• Imaging Science in Dentistry
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• v.47 no.3
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• pp.199-207
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• 2017

Purpose: Evaluation of alveolar bone is important in the diagnosis of dental diseases. The periodontal ligament space is difficult to clearly depict in cone-beam computed tomography images because the reconstruction filter conditions during image processing cause image blurring, resulting in decreased spatial resolution. We examined different reconstruction filters to assess their ability to improve spatial resolution and allow for a clearer visualization of the periodontal ligament space. Materials and Methods: Cone-beam computed tomography projections of 2 skull phantoms were reconstructed using 6 reconstruction conditions and then compared using the Thurstone paired comparison method. Physical evaluations, including the modulation transfer function and the Wiener spectrum, as well as an assessment of space visibility, were undertaken using experimental phantoms. Results: Image reconstruction using a modified Shepp-Logan filter resulted in better sensory, physical, and quantitative evaluations. The reconstruction conditions substantially improved the spatial resolution and visualization of the periodontal ligament space. The difference in sensitivity was obtained by altering the reconstruction filter. Conclusion: Modifying the characteristics of a reconstruction filter can generate significant improvement in assessments of the periodontal ligament space. A high-frequency enhancement filter improves the visualization of thin structures and will be useful when accurate assessment of the periodontal ligament space is necessary.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.912-921
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• 2021

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.149-161
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• 2022

Purpose To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST). Materials and Methods A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis. Results In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (-), progesterone receptor (PR) (-), human epidermal growth factor 2 (HER2) (+), and Ki-67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki-67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (-), PR (-), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status. Conclusion Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.