• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.20 no.2
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• pp.163-167
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• 1990

The author has evaluated the panoramic image of the mandibular condyle according to its horizontal condylar angle (0˚, 10˚, 20°, 30°, 40°) and mandibular position (standard, 25㎜ forward and reverse position). The results were as follows: 1. The larger the horizontal condylar angle was, the larger the horizontal magnification was in all positions. 2. In case of small horizontal condylar angle, profile view could be obtained in 25㎜ forward and reverse position. 3. In case of large horizontal condylar angle, profile view could not be obtained in any positions.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.144-159
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• 2014

Techniques for making a single panoramic image using multiple pictures are widely studied in many areas such as computer vision, computer graphics, etc. The panorama image can be applied to various fields like virtual reality, robot vision areas which require wide-angled shots as an useful way to overcome the limitations such as picture-angle, resolutions, and internal informations of an image taken from a single camera. It is so much meaningful in a point that a panoramic image usually provides better immersion feeling than a plain image. Although there are many ways to build a panoramic image, most of them are using the way of extracting feature points and matching points of each images for making a single panoramic image. In addition, those methods use the RANSAC(RANdom SAmple Consensus) algorithm with matching points and the Homography matrix to transform the image. The SURF(Speeded Up Robust Features) algorithm which is used in this paper to extract featuring points uses an image's black and white informations and local spatial informations. The SURF is widely being used since it is very much robust at detecting image's size, view-point changes, and additionally, faster than the SIFT(Scale Invariant Features Transform) algorithm. The SURF has a shortcoming of making an error which results in decreasing the RANSAC algorithm's performance speed when extracting image's feature points. As a result, this may increase the CPU usage occupation rate. The error of detecting matching points may role as a critical reason for disqualifying panoramic image's accuracy and lucidity. In this paper, in order to minimize errors of extracting matching points, we used $3{\times}3$ region's RGB pixel values around the matching points' coordinates to perform intermediate filtering process for removing wrong matching points. We have also presented analysis and evaluation results relating to enhanced working speed for producing a panorama image, CPU usage rate, extracted matching points' decreasing rate and accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.20-29
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• 2014

Density is applied both three major macroscopic traffic variables (traffic volume, speed, and density) and two measures of effectiveness (MOE) for level of service (LOS) on highway (density and V/C). Especially, it is known for the most accurate MOE on evaluating the LOS of highway. Despite such importance, there is a lack of study on density relatively than other variables for its difficulty of measurement. Existing density estimation methods have some limitations such as density values of same traffic flow vary with collecting time. In this study, we researched actual density measuring method with panoramic image, after each CCTV images in the Sapaesan Tunnel on Seoul Ring Expressway are matched into one panoramic image. Analysis through the Central Limit Theorem shows that density of 24 1 km-images, which means 24 second, applies traffic situation well. That is to say that reasonable density value regardless of collecting time, and practical density which represents actual traffic flow can be taken in case of measuring density by suggested collecting cycle.

• Imaging Science in Dentistry
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• v.35 no.3
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• pp.121-125
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• 2005

Purpose : The purpose of this study was to investigate whether the fractal dimension of successive panoramic radiographs of bone after implant placement is useful in the characterization of structural change in alveolar bone. Materials and Methods. Twelve subjects with thirty-five implants were retrospectively followed-up from one week to six months after implantation. Thirty-six panoramic radiographs from twelve patients were classified into 1 week, 1-2 months and 3-6 months after implantation and digitized. The windows of bone apical and mesial or distal to the implant were defined as periapical region of interest (ROI) and interdental ROI; the fractal dimension of the image was calculated. Results There was not a statistically significant difference in fractal dimensions during the period up to 6 months after implantation. The fractal dimensions were higher in 13 and 15mm than 10 and 11.5mm implant length at interdental ROIs in 3-6 months after implantation (P<0.01). Conclusion : Longer fixtures showed the higher fractal dimension of bone around implant. This investigation needs further exploration with large numbers of implants for longer follow-up periods.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.105-107
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• 2024

The author herein presents an unusual case of eustachian tube calcification masquerading as loose radiopacities in the temporomandibular joints on a panoramic image, creating a diagnostic challenge. The patient, a 72-year-old woman, presented to the dental service for implant treatment to improve her masticatory function. A cone-beam computed tomography scan was performed and reviewed by a board-certified oral and maxillofacial radiologist. The scan showed no evidence of calcifications in the temporomandibular joints; however, it revealed nodular calcifications within the cartilaginous portion of the eustachian tube bilaterally. Additionally, this report briefly reviews the differential diagnosis of calcified loose bodies in the temporomandibular joint and provides information that needs to be reinforced periodically.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.169-174
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• 2020

Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• Journal of IKEEE
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• v.19 no.4
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• pp.460-464
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• 2015

This paper proposes a panoramic image stitching that operates in real time at the embedded environment by applying ROI and PROSAC algorithm. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains complicated operations and a lots of data, at the overall image to detect feature points. Also it applies RANSAC algorithm to remove outliers, so that an additional verification time is required due to its randomness. In this paper, unnecessary data are eliminated by setting ROI based on the characteristics of panorama images, and PROSAC algorithm is applied for removing outliers to reduce verification time. The proposed method was implemented on the ORDROID-XU board with ARM Cortex-A15. The result shows an improvement of about 54% in the processing time compared to the conventional method.

• Imaging Science in Dentistry
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• v.45 no.3
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• pp.175-180
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• 2015

Purpose: This study was performed to evaluate the reliability of the identification of anatomical landmarks in panoramic and lateral cephalometric radiographs on a standard medical grade picture archiving communication system (PACS) monitor and a tablet computer (iPad 5). Materials and Methods: A total of 1000 radiographs, including 500 panoramic and 500 lateral cephalometric radiographs, were retrieved from the de-identified dataset of the archive of the Section of Oral and Maxillofacial Radiology of the University Of Connecticut School Of Dental Medicine. Major radiographic anatomical landmarks were independently reviewed by two examiners on both displays. The examiners initially reviewed ten panoramic and ten lateral cephalometric radiographs using each imaging system, in order to verify interoperator agreement in landmark identification. The images were scored on a four-point scale reflecting the diagnostic image quality and exposure level of the images. Results: Statistical analysis showed no significant difference between the two displays regarding the visibility and clarity of the landmarks in either the panoramic or cephalometric radiographs. Conclusion: Tablet computers can reliably show anatomical landmarks in panoramic and lateral cephalometric radiographs.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.19 no.1
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• pp.25-29
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• 1989

The author has evaluated the panoramic image clarity of the midfacial anatomic structures in dry skull according to the skull position. The radiopaque markers were attached to the anatomic structures: infraorbial rim, upper and lower borders of zygomatic arch, pterygomaxillary fissure, lateral pterygoid plate, pyriform aperture of nasal cavity, lateral wall of maxilla, orbital floor, infraorbital foramen, and nasal floor. Position of the skull were divided into four groups. standard, 25mm forward, chin-down, chin-up position. The results were as follows: 1. The pyriform aperture of nasal cavity, lateral wall of the maxilla, orbital floor, infraorbital foramen and nasal floor did net cast any discernible image. 2. Nearly all images of midfacial structures were blurred in the chin-up position. 3. The forward position provided good visualization of the maxillary sinus. 4. The chin-down position provided good visualization of the zygomatic arch, pterygomaxillary fissue, and lateral pterygoid plate.