• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.443-446
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• 2011

본 연구는 컴퓨터 단층촬영 영상 (CT, Computed Tomography Image)에서 치열궁 (Dental Arch)을 자동으로 검출하는 기법을 제안한다. 제안된 기법에서는 3차원 컴퓨터 단층촬영 영상을 입력 받고 영역 확장법을 이용하여 하악을 분할 한 후 하악의 단면에서 전체적인 치아의 영역을 분할을 한다. 치아의 영역에서 세선화 작업을 거친 후 곡선 정합법을 이용하여 최종 치열궁을 검출한다. 실험 데이터로 두개골 컴퓨터 단층 촬영 데이터를 사용하였다. 본 연구는 치과 영상 데이터로부터 파노라마 영상을 얻는데 이용 될 수 있고 치과 분야의 질병 진단 및 진찰에 이용될 것으로 기대된다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.1001-1007
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• 2014

This paper proposes a method to reduce operation time using discrete cosine transform and to improve image quality by the DC gain correction. Conventional filtered back projection (FBP) filtering in the frequency domain using Fourier transform, but the filtering process uses complex number operations. To simplify the filtering process, we propose a filtering process using discrete cosine transform. In addition, the image quality of reconstructed images are improved by correcting DC gain of sinograms. To correct the DC gain, we propose to find an optimum DC weight is defined as the ratio of sinogram DC and optimum DC. Experimental results show that the proposed method gets better performance than the conventional method for phantom and clinical CT images.

• Journal of Yeungnam Medical Science
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• v.18 no.1
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• pp.123-137
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• 2001

Background: The purpose of this study is to evaluate the accuracy of measurements obtained from 3-dimensional computerized tomography and 3-dimensional cephalogram constructed by using the frontal and lateral cephalogram of six human dry skulls. Materials and Methods: After CT scans and each cephalograms were taken, 3-dimensional coordinates (X, Y, Z) of landmarks were obtained using computer programs. In this study, the accuracy of both methods were determined by means of 14 linear measurements compare with caliper measurements. Results: The standard deviation of landmarks of 3-dimensional CT and 3-dimensional cephalogram were 0.23 mm, and 0.30 mm in X axis, 0.27 mm and 0.25 mm in Y axis, and 0.27 mm and 0.31 mm in Z axis. In both methods, the standard deviation were less than 0.5 mm in all landmarks, and the most of landmarks showed less than 1 mm in range. Concerning the accuracy, the mean difference between 3-dimensional CT and manual measurements was 0.33 mm, and 1.13 mm between 3-dimensional cephalogram and manual measurements. The distance between RGo and LGo showed the largest difference (2.03 mm). There were highly significant, and large correlation with manual measurements in both methods (p<0.01). Conclusion: It is concluded that closeness of repeated measures to each skulls reveal the precision of both methods. Computerized tomography and cephalogram for 3-dimensional measurement of maxillofacial structure are equivalent in quality to caliper measurements.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.39 no.6
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• pp.633-640
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• 2001

Statement of problem. Relatively low success rate of root analogue implant system was supposed to be due to the time duration between extraction and implant installation. The use of three-dimensional computer tomography and the reconstruction of objects using rapid prototyping methods would be helpful to shorten this time. Purpose. This aim of this study was to evaluate the application possibility of the 3-dimensional computer tomography and the rapid prototyping to root analogue implants. Material and methods. Ten single rooted teeth were prepared. Width and height of the teeth were measured by the marking points. This was followed by CT scanning, data conversion and rapid prototyping model fabrication. 2 methods were used; fused deposition modelling and stereolithography. Same width and height of this models were measured and compared to the original tooth. Results. Fused deposition modelling showed an enlarged width and reduced height. The stereolithography showed more exact data compared with the fused deposition modelling. Smaller standard deviation were recorded in the stereolithographic method. Overall width error from tooth to rapid prototyping was 7.15% in fused deposition modelling and 0.2% in stereolithography. Overall height showed the tendency of reducing dimensions. Conclusion. From the results of this study, stereolithography seems to be very predictable method of fabricating root analogue implant.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.252-253
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• 2019

Corrosion of rebars in reinforced concrete structures is a major factor that shortens the life of the structure. As corrosion progresses, the adhesion between the concrete tissues and the rebar decreases and the cracks in the concrete due to the expansion of the oxide intensify. Although it is necessary to measure corrosion behavior of rebars inside the concrete to measure degradation of structures due to rebar corrosion, no studies have been conducted to measure corrosion of rebars in In-situ state. In this study, corrosion of rebars in reinforced concrete specimens was attempted to be quantified using micro-computer tomography. Since corrosion of concrete takes several months per 10mm of cover, accelerated corrosion techniques were applied. Accelerated corrosion on the specimen was conducted by applying a 10 V magnetic field to the buried rebar and external electrodes with the specimen submerged in a 10% calcium chloride solution. The experiment found that within two weeks, more than 40% of rebar reduction occurred, and the cracks in the radial cracks occurred through the concrete structure, leading to the transfer of the oxide produced through the cracks to the surface of the specimen.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.465-468
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• 1997

A Study on Computer Assisted Diagnosis (CAD) system extract ing lung cancer part from Digital X-ray Computerized Tomography(CT) image is discussed in this paper. It is very crucial to segment the image of lung into the three organ area such as inside, outside and the hilum so that the variant image processing algorithm can be applied an each area respectively. In this paper, the efficient algorithm extracting lung cancer part is proposed with characterizing lung hilum part and its associated vessel patterns.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.45-60
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• 2014

Dentist can obtain 3D anatomical information without distortion and information loss by using dental Computed Tomography scan images on line, and also can make the preoperative plan of implant placement or orthodontics. It is essential to segment individual tooth for making an accurate diagnosis. However, it is very difficult to distinguish the difference in the brightness between the dental and adjacent area. Especially, the root of a tooth is very elusive to automatically identify in dental CT images because jawbone normally adjoins the tooth. In the paper, we propose a method of automatically tooth region segmentation, which can identify the root of a tooth clearly. This algorithm separate the tooth from dental CT scan images by using Seeded Region Growing method on dental crown and by using Level-set method on dental root respectively. By using the proposed method, the results can be acquired average 19.2% better accuracy, compared to the result of the previous methods.