• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.385-388
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• 2011

Patients' privacy protection is a heated issue in medical business, as medical information in digital format transmit everywhere through networks without any limitation. A current protection method for brain images is to deface from the brain image for patient's privacy. However, the defacing process often removes important brain voxels so that the defaced brain image is damaged for medical analysis. An ad-hoc method is proposed to conceal patient's identification by adding cylindrical mask, while the brain keep all important brain voxels. The proposed lossless deformation of brain image is verified not to loose any important voxels. Futhermore, the masked brain image is proved not to be recognized by others.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.248-251
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• 1995

Nowadays, the internal images of a human body can be easily provided by the ultrasound imaging, the X-ray CT, or the MRI device, among which the ultrasound imaging device has good resolution for soft tissues of a human body compared with the other devices. Furthermore, the use of ultrasound imaging devices will increase in future especially in the obstetrics, territory, since it does not give harm to the human body. Although several techniques have been investigated until now in order to extract organs from ultrasound images, very few of them have achieved satisfactory results because of low contrast and high noise nature of images. This paper proposes a technique for automatic extraction of the gall bladder area from ultrasound images. The proposed technique first extracts a small reliable area of a gall bladder from an ultrasound image employing smoothing, binarization, expanding and shrinking, and labeling, and then expands the area referring to the binarized version of the original image. The technique is examined its performance by real ultrasound images of a gall bladder and satisfactory results are obtained. Some problems to be solved are discussed finally.

• Korean Journal of Digital Imaging in Medicine
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• v.7 no.1
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• pp.33-38
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• 2005

Modern hospitals have been greatly facilitated with information technology (IT) such as hospital information system (HIS). One of the most prominent achievements is medical imaging and image data management so-called Picture Archiving and Communication Systems (PACS). Due to inevitable use of diagnostic images (such as X-ray, CT, MRI), PACS made tremendous impact not only on radiology department but also nearly all clinical departments for exchange and sharing image related clinical information. There is no doubt that better use of PACS leads to highly efficient clinical administration and hospital management. However, due to rapid and widespread acceptance of PACS storage and management of digitized image data in hospital introduces overhead and bottleneck when transferring images among clinical departments within and/or across hospitals. Despite numerous technical difficulties, financing for installing PACS is a major hindrance to overcome. In addition, a mirroring or a clustering backup can be used to maximize security and efficiency, which may not be considered as cost-effective approach because of extra hardware expenses. In this study therefore we have developed a new based on grid of distributed PACS in order to balance between the cost and network performance among multiple hospitals.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.294-298
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• 1997

In the magnetic resonance imaging, the fast spin echo imaging technique is a widely used clinical imaging method, since its scanning time is much shorter than the conventional spin echo imaging and it gives the almost same image quality. However, the fast spin echo technique has two times longer imaging time or the dual echo acquisition which can obtain a spin density image and a $T_2$-weighted image simultaneously. To overcome such a drawback, this paper proposes a new fast dual echo imaging technique which can give the same quality images at the single echo imaging time. The proposed technique reduces the imaging time by overlapping most of echo train data for each image reconstruction. In order to verify its validity and usability the human head experimental results which were obtained at the 0.3T permanent MRI system are presented.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.267-268
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• 1998

In this paper, we analysis 3 types of magnetic resonance image for determining whether brain infarction period is hyperacute or not. If its peirod is hyperacute, we can predict brain infarction transition direction. We use EPI(Echo Planar Image) for prediction of brain infarction transition direction. EPI is a good image for detecting brain infarction because EPI can detect the moving of water in brain which play an important role in deciding method of medical treatment. We utilize characteristics of 3 type of MRI and their relation in brain infarction patient for determining brain infarction period. By this method, we obtain each period characteristics and predict brain infarction transition direction more accurately comparing past method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4461-4475
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• 2017

Recently, introduction of the hospital information system has remarkably improved the efficiency of health care services within hospitals. Due to improvement of the hospital information system, the issue of integration of medical information has emerged, and attempts to achieve it have been made. However, as a preceding step for integration of medical information, the problem of searching the same patient should be solved first, and studies on patient identification algorithm are required. As a typical case, similarity can be calculated through MPI (Master Patient Index) module, by comparing various fields such as patient's basic information and treatment information, etc. but it has many problems including the language system not suitable to Korean, estimation of an optimal weight by field, etc. This paper proposes a method searching the same patient using MRI information besides patient's field information as a supplementary method to increase the accuracy of matching algorithm such as MPI, etc. Unlike existing methods only using image information, upon identifying a patient, a highest weight was given to physical information of medical image and set as an unchangeable unique value, and as a result a high accuracy was detected. We aim to use the similarity measurement result as secondary measures in identifying a patient in the future.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.283-290
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• 1999

Because MR(Magnetic Resonance) slice images have much information of functions about body organs, it is very effeclive for diagnoses lo analyze and visualize MR slice images. A visuahzation process is composed of medical image acquisition, preprocessmg, segmentation, inlerpolation, rendering. Segmentation and interpolation among thenl ,1re currenl hot topics because of MR slice image imperfections. This paper proposes a method for segmentalion, mlerpolation respectively and addresses 3 D-visualizmg of a head. We segmented head tissues uomg otructural knowledge of head studied by clinical experiments sequentially. We improved the dynamic elastic inlerpolation to Utilize in concave conlour. We compared the proposed segmentation method and the interpolation method with other methods.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.9-16
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• 2024

AI or Artificial Intelligence has been a significant tool used in the organisational backgrounds for an effective improvement in the management methods. The processing of the information and the analysis of the data for the further achievement of heightened efficiency can be performed by AI through its data analytics measures. In the medical field, AI has been integrated for an improvement within the management of the medical services and to note a rise in the levels of customer satisfaction. With the benefits of reasoning and problem solving, AI has been able to initiate a range of benefits for both the consumers and the medical personnel. The main benefits which have been noted in the integration of AI would be integrated into the study. The issues which are noted with the integrated AI usage for the medical sector would also be identified in the study. Medical Image Processing has been seen to integrate 3D image datasets with the medical industry, in terms of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). The usage of such medical devices have occurred in the diagnosis of the patients, the development of guidance towards medical intervention and an overall increase in the medical efficiency. The study would focus on such different tools, adhered with AI for increased medical improvement.