• Korean Medical Education Review
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• v.13 no.2
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• pp.3-7
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• 2011

The status of medical doctors is relatively high in society. However, in spite of this acknowledged status, physicians are not aware of the extent to which they have the ability to care for patients or how much effort they should make to meet people's expectations. Therefore, we should examine what society asks of doctors and how doctors need to be educated to meet the expectations of society. In this article, the author asserts that physicians need four skills. First, doctors should know how to speak and communicate. In the work of a doctor, language is the most important for tasks such as understanding texts, communication with patients, analyzing data, and starting new projects. Second, doctors should have intuition. In a doctor's medical judgment, intuition is very important and it can initiate from an educated guess. In other words, good intuition can be developed based on a good educated guess, which in turn can derive from one's explored knowledge, communication with one's inner dialogues, and good interpretation skill. Third, doctors should have creativity. Doctors should produce an image about patients from intuition, and those intuitions are based on creativity. Usually, students in medical school have creative ability; therefore, the instructor should facilitate their learning to connect this creativity to free imagination ability and medical skills. Fourth, doctors should be humane. Patients want to communicate with doctors about their disease and further about their lives. The reason why a humane doctor is important is that this humane approach itself could cure patients and reduce their pain. When a doctor's humane attitude is realized in the hospital, the patients and doctors could be pleased sincerely.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.2
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• pp.125-133
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• 2019

A microscope is the fundamental research and diagnostic apparatus for clinical investigation of signaling transduction, morphological changes and physiological tracking of cells and intact tissues from patients in the biomedical laboratory science. Proper use, care and maintenance of microscope with comprehensive understanding in mechanism are fully requested for reliable image data and accurate interpretation for diagnosis in the clinical laboratory. The standard operating procedure (SOP) for light microscopes includes performance procedure, brief information of all mechanical parts of microscopes with systematic troubleshooting mechanism depending on the laboratory capacity. Maintenance program encompasses cleaning objective, ocular lenses and inner optics; replacement and calibration of light source; XY sample stage management; point spread function (PSF) measurement for confocal laser scanning microscope (CLSM); quality control (QC) program in fluorescent microscopy; and systematic troubleshooting. Laser safety is one of the concern for medical technologists engaged in CLSM laboratory. Laser safety guideline based on the laser classification and risk level, and advisory lab wear for CLSM users are also expatiated in this overview. Since acquired image data presents a wide range of information at the moment of acquisition, well-maintained microscopes with proper microscopic maintenance program are impulsive for its interpretation and diagnosis in the clinical laboratory.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.258-265
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• 2021

High levels for noise and a loss of true signal make the quantitative interpretation of nuclear medicine (NM) images difficult. An application of profile optimization using a sigmoidal function in this study was used to acquire the NM images with high quality. And the images were acquired by using three kinds of reconstruction method using each same sinogram: a standard filtered back-projection (FBP), an iterative reconstruction (IR) technique, and the sigmoidal function profile optimization (SFPO). Comparison of image according to reconstruction method was performed to show a superiority of the SFPO for imaging. The images reconstructed by using the SFPO showed an average of 1.49 times and of 1.17 times better in contrast than the results obtained using the standard FBP and the IR technique, respectively. Higher signal to noise ratios were obtained as an average of 12.30 times and of 3.77 times than results obtained using the standard FBP and the IR technique, respectively. This study confirms that reconstruction with SFPO (vs FBP and vs IR) can lead to better lesion detectability and characterization with noise reduction. It can be developed for future reconstruction technique for the NM imaging.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• Progress in Medical Physics
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• v.24 no.3
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• pp.176-182
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• 2013

Through this research, we measure the data for several SUVs such as SUVLBM, SUVBW, and SUVBSA using volume of interest in order to enhance the diagnostic level in whole-body image for healthy examinees via F-18 FDG PET/CT. Maximum value, mean value, standard deviation, and threshold value for each SUVs are shown. The measurement of SUVs are carried out with 31 examinees who have taken whole-body examination with F-18 FDG PET/CT from July, 2012 to August, 2012. To secure the preciseness of measurement, we selected 26 healthy examinees as a subject of measurement according to diagnostic view of a nuclear-medical doctor. We see from the measurement of SUVs of PET/CT that the value of SUVBW is hightest and followed by SUVLBM and SUVBSA in turn regardless of the use of contrast media. By comparing the SUVLBM-maximum data for the group used contrast media with those for the group used no contrast media, there found a trend that the measured values increase when the contrast media are used. Among them, liver, aorta, lumbar-5, and Cerebellum exhibit significant difference (p<0.05). We conclude that our data for SUVs would be basic references in overall image interpretation, and hope that the research using VOI would be active.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.150-169
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• 2023

Multiple myeloma (MM) is a malignant hematologic disease caused by the proliferation of clonal plasma cells in the bone marrow, and its incidence is increasing in Korea. With the development of treatments for MM, the need for early diagnosis and treatment has emerged. In recent years, the International Myeloma Working Group (IMWG) has been constantly revising the laboratory and radiological diagnostic criteria for MM. In addition, as whole-body MRI (WBMR) has been increasing used in the diagnosis and treatment response evaluation of patients with MM, the Myeloma Response Assessment and Diagnosis System (MY-RADS) was created to standardize WBMR image acquisition techniques, image interpretation, and response evaluation methods. Radiologists need to have a detailed knowledge of the features of MM for accurate diagnosis. Thus, in this review article, we describe the imaging method for MM according to the latest IMWG guidelines as well as the image acquisition and response evaluation technique for WBMR according to MY-RADS.

• Progress in Medical Physics
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• v.20 no.2
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• pp.51-61
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• 2009

A Compton camera, which is based on the geometrical interpretation of Compton scattering, is a very promising gamma-ray imaging device considering its several advantages over the conventional gamma-ray imaging devices: high imaging sensitivity, 3-D imaging capability from a fixed position, multi-tracing functionality, and almost no limitation in photon energy. In the present study, a Monte Carlo-based, user-friendly Compton imaging simulator was developed in the form of a graphical user interface (GUI) based on Geant4 and $MATLAB^{TM}$. The simulator was tested against the experimental result of the double-scattering Compton camera, which is under development at Hanyang University in Korea. The imaging resolution of the simulated Compton image well agreed with that of the measured image. The imaging sensitivity of the measured data was 2~3 times higher than that of the simulated data, which is due to the fact that the measured data contains the random coincidence events. The performance of a stacking-structure type Compton camera was evaluated by using the simulator. The result shows that the Compton camera shows its highest performance when it uses 4 layers of scatterer detectors.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.279-280
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• 1998

A new algorithm is developed that can reduce the metal artifact on CT caused by pedicle screws. Metal artifact has been recognized as a major problem in precise reading of CT images. In particular, spine surgeons have been bothered with the artifact appearing on CT taken after pedicle screw insertion. To reduce the artifact, our new algorithm first finds the center line from CT images, and then overlays an exact size screw image on the CT. The exact screw is obtained from an actual design specifications of screw, and the CT images are processed to maximize bone margins while minimizing screw images through adjusting the window width and level. 실험 결과 단순한 Window W/L 조절로는 해결되지 않는군요. This algorithm provides spine surgeons with more accurate CT images and thus better interpretation of CT to ascertain the success or failure of pedicle screw insertion.

• Journal of Biomedical Engineering Research
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• v.26 no.2
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• pp.73-86
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• 2005

We need to flatten the brain cortex to smooth surface, sphere, or 2D plane in order to view the buried sulci. The rendered 3D surface of the segmented white matter and gray matter does not have the topology of a sphere due to the partial volume effect and segmentation error. A surface without correct topology may lead to incorrect interpretation of local structural relationships and prevent cortical unfolding. Although some algorithms try to correct topology, they require heavy computation and fail to follow the deep and narrow sulci. This paper proposes a method that corrects topology of the rendered surface fast, accurately, and automatically. The proposed method removes fractions beside the main surface, fills cavities in the inside of the main surface, and removes handles in the surface. The proposed method to remove handles has three-step approach. Step 1 performs smoothing operation on the rendered surface. In Step 2, vertices of sphere are gradually deformed to the smoothed surfaces and finally to the boundary of the segmented white matter and gray matter. The Step 2 uses multi-resolutional approach to prevent the deep sulci from geometrical intersection. In Step 3, 3D binary image is constructed from the deformed sphere of Step 2 and 3D surface is regenerated from the 3D binary image to remove intersection that may happen. The experimental results show that the topology is corrected while principle sulci and gyri are preserved and the computation amount is acceptable.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.