• Progress in Medical Physics
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• v.33 no.2
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• pp.11-24
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• 2022

X-ray detection has widely been applied in medical diagnostics, security screening, nondestructive testing in the industry, etc. Medical X-ray imaging procedures require digital flat detectors operating with low doses to reduce radiation health risks. Recently, metal halide perovskites (MHPs) have shown great potential in high-performance X-ray detection because of their attractive properties, such as strong X-ray absorption, high mobility-lifetime product, tunable bandgap, low-temperature fabrication, near-unity photoluminescence quantum yields, and fast photoresponse. In this paper, we review and introduce the development status of new perovskite X-ray detectors and imaging, which have emerged as a new promising high-sensitivity X-ray detection technology. We discuss the latest progress and future perspective of MHP-based X-ray detection in medical imaging. Finally, we compare the conventional detection methods with quantum-enhanced detection, pointing out the challenges and perspectives for future research directions toward perovskite-based X-ray applications.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.897-902
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• 2023

The purpose of this paper is to analyze the characteristics of Silicon Photomultiplier (SiPM) for the realization of high-sensitivity radiation detection in portable detectors. Portable X-ray detectors offer the advantage of quickly accessing the patient's location and obtaining real-time images, allowing physicians to perform rapid diagnoses. However, this mobility comes with challenges in achieving accurate radiation detection. In existing detectors, SiPM is used for a simple purpose of detecting X-ray triggers. To verify the feasibility of high-sensitivity X-ray detection through SiPM, seven types of SiPM sensors were compared and selected, and their characteristics were analyzed. The SiPM used in the final test demonstrated the ability to distinguish signals at the ultra-low radiation level of 10 nGy, and it was observed that the slope of the signal rise curve varies with the X-ray tube voltage. Utilizing the characteristics of SiPM, which exhibits changes in signal level and duration with X-ray dose, it appears possible to achieve high-sensitivity measurements for X-ray detection.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1433-1447
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• 2022

Recently, as the demand for physical security technology to prevent leakage of technical and business information of companies and public institutions increases, the high tech companies are operating X-ray security checkpoints at building entrances to protect their intellectual property and technology. X-ray security checkpoints are operated to detect cameras and storage media that may store or leak important technologies in the bags of people entering and leaving the building. In this study, we propose an X-ray security checkpoint system that automatically detects a storage medium in an X-ray image using a deep learning based object detection method. The proposed system consists of an edge computing unit and a cloud-computing unit. We employ the RetinaNet for automatic storage media detection in the X-ray security checkpoint images. The proposed approach achieved mAP of 95.92% on private dataset.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.33.2-33.2
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• 2010

Galaxy clusters are known to be very bright in X-ray and contain a large number of X-ray point sources within the X-ray emission. However, due to the fluctuations of the X-ray emission, it is very difficult to detect faint X-ray sources and to extract accurately the photometric properties of the X-ray point sources in galaxy clusters. In addition, the most X-ray telescopes show spatially varying point spread function (PSF) and suffer from severe vignetting. The Chandra Archival Survey of Galaxy Clusters project is a wide-area ($\sim40deg^2$) survey of serendipitous Chandra X-ray sources in galaxy cluster fields, containing ~58,000 X-ray point sources in ~800 Chandra ACIS observations of ~600 galaxy clusters. This project aim to investigate the density environmental effects on the physical properties of the X-ray point sources, comparing physical properties of the X-ray point sources in galaxy clusters to those in typical fields. To utilize the sensitivity and detection probability of the X-ray point sources in galaxy clusters, we perform extensive Monte-Carlo simulations. In this poster, we compare the detection probability of the X-ray point sources in galaxy clusters to that of typical fields, and discuss quantitatively the difference between them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.15-21
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• 2019

X-rays with shorter wavelengths than ultraviolet light have very good penetration power. It is convergence in industrial and medical fields has been used a lot. n particular, in the industrial field, various researches have been conducted on the detection of foregin body inside metal that can occur in the production process of products such as metal using x-ray, a non-destructive inspection device. Detectors are becoming increasingly popular for the popularization of DR (Digital Radiography) photography methods that digitally acquire X-ray video images. However, there are cases where foreign body detection is impossible depending on the sensor noise and sensitivity inside the detector. When producing a metal product, since the defective rate of the produced product may increase due to contamination of the foreign body, accurate detection is necessary. In this paper, we provide a correction model for X-ray images acquired in order to improve the efficiency of defect detection such as foreign body inside metal. When applied to defect detection in the production process of metal products through the proposed model, it is expected that the detection of product defects can be processed accurately and quickly.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.1035-1041
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• 2016

Internal cracks in products are invisible and can lead to fatal crashes or damage. Since X-rays can penetrate materials and be attenuated according to the material’s thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

• Journal of Radiation Protection and Research
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• v.43 no.4
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• pp.137-142
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• 2018

Background: Gamma-ray detectors having a thin window of a material with low atomic number can increase the true coincidence summing effects for radionuclides emitting X-rays or gamma-rays. This effect can make efficiency calibration or spectrum analysis more complicated. In this study, a Cu shield was tested as an X-ray filter to neglect the true coincidence summing effect by X-rays and gamma-rays in gamma-ray spectrometry, in order to simplify gamma-ray energy spectrum analysis. Materials and Methods: A Cu shield was designed and applied to an n-type high-purity germanium detector having an $X-{\gamma}$ summing effect during efficiency calibration. This was tested using a commercial, certified mixed gamma-ray source. The feasibility of a Cu shield was evaluated by comparing efficiency calibration results with and without the shield. Results and Discussion: In this study, the thickness of a Cu shield needed to avoid true coincidence summing effects due to $X-{\gamma}$ was tested and determined to be 1 mm, considering the detection efficiency desired for higher energy. As a result, the accuracy of the detection efficiency calibration was improved by more than 13% by reducing $X-{\gamma}$ summing. Conclusion: The $X-{\gamma}$ summing effect should be considered, along with ${\gamma}-{\gamma}$ summing, when a detection efficiency calibration is implemented and appropriate shielding material can be useful for simplifying analysis of the gamma-ray energy spectra.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.334-343
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• 1996

Quality evaluation for food and agricultural products have always been one of the most elusive problems associated with the handling , processing and marketing in a food plant production. In order to detect physical foreign materials in food and agricultural products, non-destructive techniques have been developed for many years. Application of X-ray system to detect physical foreign materials in food and agricultural products could be considered to be a high potential method. Especially , it is impossible to detect internal physical foreign materials by visual inspections. In this study, it was tried to be applied for two different X-ray devices. Soft X-ray system with CdTe sensor and X-ray CT scanner were evaluated for advantage of the detection of non-meltallic foreign materials in food and agricultural products . Though the soft X-ray is not a high energy radiation, it is possible to detect small different density in a material. The CdTe sensor has a high resolution for t e soft X-ray energy region. The density characteristics of foods and foreign material were expressed region. The density characteristics of foods and foreign materials were expressed as a soft X-ray energy spectrum. The energy spectrum was analyzed by a personal computer with a multi-channel analyzer. X-ray CT scanner can provide visual image and analyze by three dimensional information inside food and agricultural products. The X-ray CT scanner using as a medical equipment was used to detect a foreign material. The density characteristics of food and foreign materials in food were tried to be detected by the threshold value on the basis of the CT numbers. The soft X-ray absorption characteristics for acrylin plates and distilled water were obtained and could be found the possibility of detecting a small physical foreign materials such as a plastic wrapping film , a stone and grasshopper in food and agricultural products.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.