• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.167-170
• /
• 1995

An frequency modulated (FM) DANTE pulse sequence generates a quadratic phase toward the transverse of image by an FM RF pulse. In the image of a serious susceptibility effect, the phase due to the difference of the susceptibility in the pixel occurs susceptibility error which arise signal loss. But the signal loss due to the susceptibility effect in the pixel is reduced when the quadratic phase adds in the pixel. In this paper, we have generated a quadratic function toward the transverse (X-Y) using FM DANTE sequence and the susceptibility effect is reduced in the gradient echo (GE) imaging. Computer simulation and experimental results is obtained by using a whole-body KAIS 2.0T NMR system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.2 no.4
• /
• pp.619-626
• /
• 1998

This paper presents the features for quantitative evaluation of medical images based on the technique of image processing. In consideration of advice from medical doctor, 1 derive three features seemed to be strongly correlated with the degree of disease's advance. From comparison between each feature and evaluation value by medical doctor, our research shows that three features can be a useful aids in quantitative evaluation of lung's disease on chest X-ray images.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.1
• /
• pp.191-197
• /
• 2013

Conventional CT and MRI scans produce cross-section slices of body that are viewed sequentially by radiologists who must imagine or extrapolate from these views what the 3 dimensional anatomy should be. By using sophisticated algorithm and high performance computing, these cross-sections may be rendered as direct 3D representations of human anatomy. The 2D medical image analysis forced to use time-consuming, subjective, error-prone manual techniques, such as slice tracing and region painting, for extracting regions of interest. To overcome the drawbacks of 2D medical image analysis, combining with medical image processing, 3D visualization is essential for extracting anatomical structures and making measurements. We used the gray-level thresholding, region growing, contour following, deformable model to segment human organ and used the feature vectors from texture analysis to detect harmful cancer. We used the perspective projection and marching cube algorithm to render the surface from volumetric MR and CT image data. The 3D visualization of human anatomy and segmented human organ provides valuable benefits for radiation treatment planning, surgical planning, surgery simulation, image guided surgery and interventional imaging applications.

• The Journal of Korean Society for Radiation Therapy
• /
• v.18 no.2
• /
• pp.67-73
• /
• 2006

Purpose: The number of patients receiving radiotherapy has increased every year and will keep increasing in the future. Therefore, the technique of radiotherapy is developing from day to day, as a result of it, the quantities of image and data used for radiotherapy are also considerably increasing. Therefore, there have been many difficulties in storing, keeping and managing them. Then, we developed and applied this system for improving complicated work process as well as solving these problems with the collaboration Medical Information Team. Materials and Methods: We exported its image at R & V (Record and Verify: Varis vision, Varian, USA) system and planning system after giving some code to be able to access from management system(RO) for department of radiation oncology to PACS. And, we programmed their information by using necessary information among many information included in DICOM head. Results: All images and data generated by our working environment (Simulation CT, L-gram image and internal body structure, DRR, does distribution )were realized at PACS and it became to be possible for clear image to be printed from any computer in department of radiation oncology. Conclusion: It was inevitable to use film during radiotherapy for patients in the past, however, due to the development of this system, film-less system became to be possible. Therefore, the darkroom space and its management cost in relation to the development process disappeared and it became to be unnecessary for spending tangible and intangible financial expense including human resources, time needed for finding film storing space and film and purchasing separate storing equipment for storing images. Finally, we think this system would be very helpful to handle ail complicated processes for radiotherapy and increasing efficiency of overall working conditions.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.9
• /
• pp.317-322
• /
• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.5
• /
• pp.489-497
• /
• 2001

This paper describe design and implementation of a medical multi-agent system platform called MAMI (Multi-Agent system for Medical Image), which provides intelligent medical information services. The most important component of MAMI is a medical multi-agent system platform that supports a physical environment that medical agents can be deployed. MAMI follows FIPA (Foundation for Intelligent Physical Agent)\`s agent management reference model. In MAMI, COM(Common Object Model) and XML (eXtensibel Markup Language) for encoding ACL (Agent Communication Language) are used for multi-agent communications. In MAMI, a medical staff is conceptualized as an agent and integrated with multi-agent systems. MAMI agent platform provides an infrastructure applicable to share necessary knowledge between human agents and software agents. So MAMI makes intelligent medical information services easier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.9
• /
• pp.1528-1543
• /
• 2011

With increasing the using of wireless technologies in essential fields such as the medical application, this paper proposes different scenarios for the transmission of images over wireless networks. The paper uses the IEEE ZigBee 802.15.4 for applying the proposed schemes. It is a Wireless Personal Area Network (WPAN). This paper presents a novel chaotic interleaving scheme against error bursts. Also, the paper studies the proposed interleaver with the convolutional code with different constraint lengths (K). A comparison study between the standard scheme and proposed schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes. Also, the chaotic interleaver packet-by-packet basis gives a high quality image with (K=3) and reduces the need for the complex encoder with K=7.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.1
• /
• pp.132-148
• /
• 2013

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.183-186
• /
• 1995

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.6
• /
• pp.413-421
• /
• 2015

Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) / L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera / mobile cameras.