• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Korean Journal of Digital Imaging in Medicine
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• v.5 no.1
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• pp.32-44
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• 2002

The role of radiology department has been greatly increased in the past few years as the technology in the medical imaging devices improved and the introduction of PACS (Picture Archiving and Communications System) to the conventional film-based diagnostic structure is a truly remarkable factor to the medical history. In addition, the value of using digital information in medical imaging is highly expected to grow as the technology over the computer and the network improves. However, the current medical practice, using PACS is somewhat limited compared to the film-based conventional one due to a poor image quality. The image quality is the most important and inevitable factor in the PACS environment and it is one of the most necessary steps to more wide practice of digital imaging. The existing image quality control tools are limited in controlling images produced from the medical modalities, because they cannot display the real image changing status. Thus, the image quality is distorted and the ability to diagnosis becomes hindered compared to the one of the film-based practice. In addition, the workflow of the radiologist greatly increases; as every doctor has to perform his or her own image quality control every time they view images produced from the medical modalities. To resolve these kinds of problems and enhance current medical practice under the PACS environment, we have developed a program to display a better image quality by using the ROI optical density of the existing gray level values. When the LUT is used properly, small detailed regions, which cannot be seen by using the existing image quality controls are easily displayed and thus, greatly improves digital medical practice. The purpose of this study is to provide an easier medical practice to physicians, by applying the technology of converting the H-D curves of the analog film screen to the digital imaging technology and to preset image quality control values to each exposed body part, modality and group of physicians for a better and easier medical practice. We have asked to 5 well known professional physicians to compare image quality of the same set of exam by using the two different methods: existing image quality control and the LUT technology. As the result, the LUT technology was enormously favored over the existing image quality control method. All the physicians have pointed out the far more superiority of the LUT over the existing image quality control method and highly praised its ability to display small detailed regions, which cannot be displayed by existing image quality control tools. Two physicians expressed the necessity of presetting the LUT values for each exposed body part. Overall, the LUT technology yielded a great interest among the physicians and highly praised for its ability to overcome currently embedded problems of PACS. We strongly believe that the LUT technology can enhance the current medical practice and open a new beginning in the future medical imaging.

• Progress in Medical Physics
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• v.23 no.4
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• pp.292-302
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• 2012

The patient dose in advanced radiotherapy techniques is an important issue. These methods should be evaluated to reduce the dose in diagnostic imaging for radiotherapy. Especially, the Computed Tomography in radiotherapy has been used widely; hence the CT was evaluated for dose and image in this study. The evaluations for dose and image were done in equal condition due to compare the dose and image simultaneously. Furthermore, the possibility of dose and image evaluations by using the Monte Carlo simulation MCNPX was confirmed. We made the iterative reconstruction for low dose CT image to elevate image quality with Maximum Likelihood Expectation Maximization; MLEM. The system we developed is expected to be used not only to reduce the patient dose in radiotherapy, also to evaluate the overall factors of image modalities in industrial research.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.331-342
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• 1989

This paper presents a new algorithm that extracts lung region in X-Rays and enhance.j the region. Comparing to prior algorithms that enhance whole X-Ray image, this algorithm leads more effective results. For this algorithm extracts lung region first, and enhances the lung region excluding parameters of other region. For choosing optimal threshold, we compare OTSU's mothod with the proposed method. We obtain lung boundary using contour following algorithm and Rray level searching method in gray level rescaled image. We Process histogram equalization in lung region and obtain enhanced lung image. By using the proposed algorithm, we obtain lung region effectively in chest X-Ray that need in medical image diagnostic system.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.38-41
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• 2002

Mammography is considered the single most important diagnostic tool in the early detection of breast cancer. Today's dedicated mammographic equipment, specially designed x-ray screen/film combinations, coupled with controlled film processing, produces excellent image quality and can detect very low contrast small lesions. In mammography, it is most important to produce consistent high-contrast, high-resolution images at the lowest radiation dose consistent with high image quality. Some of the major technical development milestones that have let to today's high quality in mammographic imaging are reviewed. Both the American College of Radiology Mammography Accreditation Program and the Mammography Quality Standards Act have significant impact on the improvement of the technical quality of mammographic images in the United States and worldwide. A most recent development in digital mammography has opened up avenues for improving diagnosis.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.102-104
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• 2019

Registering different kinds of clinical images widely used in diagnostic and surgery planning. However, cause of tumor growth or effected by gravity, human tissue has plenty of non-rigid deformation with clinically. Non-rigid registration allows the mapping of straight lines to curves. Therefore, such local deformation makes registration more complicated. In this work, we mainly introduce intra-subject, inter-modality registration. This paper mainly studies the nonlinear registration method of 2D medical image registration. The general medical image registration algorithm requires manual intervention, and cost long registration time. In our work to reduce the registration time in rough registration step, the barycenter and the direction of main axis of the image is calculated, which reduces the calculation amount compared with the method of using mutual information.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.1
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• pp.53-65
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• 2007

PACS has been run at the Kyung Hee University Medical Center(KHMC) since 2001, and the installation and operation of PACS have contributed to automation and quantification of KHMC's medical environment During these five years our greatest concern is how to make our own guiding principle of diagnostic monitor QA which is adapted to international standards. In accordance with the terms of 'KHMC QA Guideline', 'AAPM TG18', 'SMPTE RP133', 'DICOM Part14', 'DIN V 6868-57', 'JESRA X-0093', 'JIS Z4752-2-5' and 'KCARE', concern about quality assurance of medical images are on the increase. With the investigation of acceptance testing and quality control of international standards for medical display devices, and data collection and analysis for recommended guideline, it is reported that acceptance testing(quality control), including geometrical distortion, display reflection, luminance response, luminance uniformity, display resolution, display noise, veiling glare and color chromaticity being adequate and effective to domestic hospital environments for medical display devices and assessment methods according to each performance. Accordingly, KHMC classified the checkpoint items by period, at the time of monitor setting, monthly, quarterly, half-yearly and annually. Periodic classification of checkpoint items for monitor QA makes a good guideline for image QA/QC and useful guideline for persistent good quality of monitor.

• The Transactions of the Korea Information Processing Society
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• v.5 no.12
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• pp.3275-3284
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• 1998

In this paper, we deseribe a Medical Image Information System. Our system stores and manages 5 dimensional medical image data and provides the 3 dimensional medical data via the Internet. The Internet standard VR format. VRML(Virtual Reality Modeling Language) is used to represent the 3I) medical image data. The 3D images are reconstructed from medical image data which are enerated by medical imaging systems such ans CT(Computerized Tomography). MRI(Magnetic Resonance Imaging). PET(Positron Emission Tomograph), SPECT(Single Photon Emission Compated Tomography). We implemented the medical image information system shich rses a surface-based rendering method for the econstruction of 3D images from 2D medical image data. In order to reduce the size of image files to be transfered via the Internet. The system can reduce more than 50% for the triangles which represent the surfaces of the generated 3D medical images. When we compress the 3D image file, the size of the file can be redued more than 80%. The users can promptly retrieve 3D medical image data through the Internet and view the 3D medical images without a graphical acceleration card, because the images are represented in VRML. The image data are generated by various types of medical imaging systems such as CT, MRI, PET, and SPECT. Our system can display those different types of medical images in the 2D and the 3D formats. The patient information and the diagnostic information are also provided by the system. The system can be used to implement the "Tele medicaine" systems.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Journal of the Microelectronics and Packaging Society
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• v.21 no.4
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• pp.25-31
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• 2014

Bioimaging has advanced the field of nanomedicine, drug delivery, and tissue engineering by directly visualizing the dynamic mechanism of diagnostic agents or therapeutic drugs in the body. In particular, wide-field, planar, near-infrared (NIR) fluorescence imaging has the potential to revolutionize human surgery by providing real-time image guidance to surgeons for target tissues to be resected and vital tissues to be preserved. In this review, we introduce the principles of NIR fluorescence imaging and analyze currently available NIR fluorescence imaging systems with special focus on optical source and packaging. We also introduce the evolution of the FLARE intraoperative imaging technology as an example for image-guided surgery.