• Proceedings of the Korea Multimedia Society Conference
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• 1998.10a
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• pp.321-326
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• 1998

본 연구에서는 환자와 한의사, 방사선 전문의 간에 초고속 통신망을 이용하여 원격으로 환자의 질환 상태를 파악하고 이에 따른 처방전을 내릴 수 있도록 하는 한방 원격 검진 시스템에서 환자의 X-ray 사진, CT 사진 등의 방사선 이미지들을 DICOM 프로토콜에 의거하여 저장하고 검색할 수 있도록 해주는 시스템을 구현하였다. 본 연구에서는 이미지 처리 및 관리 모듈을 Java 언어를 이용하여 구현함으로써 방사선 전문의와 한의사들이 컴퓨터의 기종에 관계없이 웹브러우저를 사용하여 환자의 X-ray나 CT 등의 방사선 사진을 살펴볼 수 있도록 하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.367-368
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1475-1476
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• 2011

• Journal of the Korean Society of Radiology
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• v.11 no.6
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• pp.453-458
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• 2017

Computed Tomography (CT) provides information on the Diagnostic Reference Level Computed Tomography Dose Index (CTDI) and Dose Length Product (DLP) for accurate diagnosis of patients. However, it does not provide a dose change according to the table height for the diagnostic reference level provided by the CT equipment. The purpose of this study was to evaluate the image and dose according to the table height change using phantom (PMMA: Polymethyl Methacrylate) in order to find the optimal image and the minimum dose during computed tomography examination. When examining using a 32 cm PMMA phantom with the same thickness as the abdomen of an adult, there was little change in dose with table height. However, the noise evaluation of the image caused a high fluctuation of noise depending on the table height. and in the case of the 16 cm PMMA phantom, the change of the noise was small, but the dose change was about 30%. In conclusion, the location of the patient and the center of the detector are important during computed tomography (CT) examinations. In addition, table height setting is considered to be important for examinations with optimized image and minimum dose.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.249-255
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• 2015

In this study, through the additional information of the PET / CT images by utilizing the basic data of TPS clinical application on the basis of the image re-forming synthetic function, the True-D technology and MIM software for continued research and development in combination, based on the combination-work between the respective images, reducing the time and cost of useful reading in clinical wide use of image width, efficient, effective tool for tumor targeting at diagnosis and radiation therapy by use as, by using the precise therapeutic effect determination, the time taken to read in the clinical, unnecessary and expect to a can reduce the additional examination by the creation of tumor patients read reports and PACS such asWe expect to be utilized for compatibility development with other software to evaluate the performance of PET / CT equipment.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.611-614
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• 2023

갈비뼈 병변 진단 과정은 방사선 전문의가 CT 스캐너를 통해 생성된 2 차원 CT 이미지들을 해석하며 진행된다. 병변의 위치를 파악하고 정확한 진단을 내리기 위해 수백장의 2차원 CT 이미지들이 세밀하게 검토되며 갈비뼈를 분류한다. 본 연구는 이런 노동 집약적 작업의 문제점을 개선시키기 위해 Biaxial Rib Segmentation(BARS)을 제안한다. BARS 는 흉부 CT 볼륨의 관상면과 수평면으로 구성된 2 차원 이미지들을 U-Net 모델에 학습한다. 모델이 산출한 세그멘테이션 마스크들의 조합은 서로 다른 평면의 공간 정보를 보완하며 3 차원 갈비뼈 볼륨을 재건한다. BARS 의 성능은 DSC, Recall, Precision 지표를 사용해 평가하며, DSC 90.29%, Recall 89.74%, Precision 90.72%를 보인다. 향후에는 이를 기반으로 순차적 갈비뼈 레이블링 연구를 진행할 계획이다.

• Journal of Dental Rehabilitation and Applied Science
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• v.33 no.4
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• pp.278-283
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• 2017

Purpose: The aim of this study was to evaluate the interdental distances of anterior, premolar, and molar teeth at the cementoenamel junction (CEJ) and 2 mm below the CEJ in healthy natural dentition with cone-beam computerized tomography (cone-beam CT) in order to provide valuable data for ideal implant positioning relative to mesiodistal bone dimensions. Materials and Methods: Two hundred patients who visited Dental Hospital, Wonkwang University, who had natural dentition with healthy interdental papillae, and who underwent cone-beam CT were selected. The cone-beam CT images were converted to digital imaging and communication in medicine (DICOM) files and reconstructed in three-dimensional images. To standardize the cone-beam CT images, head reorientation was performed. All of the measurements were determined on the reconstructed panoramic images by three professionally trained dentists. Results: At the CEJ, the mean maxillary interdental distances were 1.84 mm (anterior teeth), 2.07 mm (premolar), and 2.08 mm (molar), and the mean mandibular interproximal distances were 1.55 mm (anterior teeth), 2.20 mm (premolar), and 2.36 mm (molar). At 2mm below the CEJ, the mean maxillary interdental distances were 2.19 mm (anterior teeth), 2.51 mm (premolar), and 2.60 mm (molar), and the mean mandibular interproximal distances were 1.86 mm (anterior teeth), 2.53 mm (premolar), and 3.01 mm (molar). Conclusion: The interdental distances in the natural dentition were larger at the posterior teeth than at the anterior teeth and also at 2 mm below the CEJ level compared with at the CEJ level. The distances between mandibular incisors were the narrowest and the distances between mandibular molars were the widest in the entire dentition.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.10a
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• pp.311.2-311
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• 2001

• The Korean Journal of Nuclear Medicine Technology
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• v.24 no.1
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• pp.15-19
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• 2020

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent ¹⁸F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.