• 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.

• The Korean Journal of Emergency Medical Services
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• v.18 no.1
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• pp.29-41
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• 2014

Purpose : The purpose of the study is investigate the images towards emergency medical technician(EMT) perceived by secondary school teachers. Methods : The data was collected from July 30 to 31, 2012. The subjects were 337 secondary school teachers. SPSS Statistics 18.0 was used for internal consistency reliability, descriptive statistical analysis and factor analysis. Results : Those who contacted with EMT consisted of 57.6% which was higher than those who had not(42.4%). Before contact, the gender and teaching subject showed a significant difference of p =.027 and p =.002, respectively. The average on the image towards EMT showed a positive points of $5.68({\pm}0.73)$ out of 7 and the most positive image was 'practical-theoretical' of $6.33({\pm}1.12)$. Factor analysis of EMT image was analyzed based on 3 factors including sincerity($5.89{\pm}0.89$), professionalism($6.02{\pm}0.89$) and familiarity($4.51{\pm}1.42$)(p <.000). The degree of familiarity showed significant differences in gender(p =.005), majoring(teaching subjects)(p =.049) and prior contact(p =.020). Conclusion : In conclusion, previous contact and recognition towards EMT could make good image making in secondary school teachers by getting well-organized promotions and professional services. This contact will make a good future counseling to the students who want to be EMTs.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1291-1298
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• 2017

Optical coherence tomography (OCT) is the most common medical imaging device with the ability to image the retina in eyes at micrometer resolution and to visualize the pathological indicators of many retinal diseases such as Age-Related Macular Degeneration (AMD) and diabetic retinopathy. Accordingly, there have been research activities to analyze and process OCT images to identify those indicators and make medical decisions based on the findings. In this paper, we use a deep convolutional neural network for analysis of OCT volume data to distinguish AMD from normal patients. We propose a novel approach where images in each OCT volume are grouped together into sub-volumes and the network is trained by those sub-volumes instead of individual images. We conducted an experiment using public data set to evaluate the performance of the proposed approach. The experiment confirmed performance improvement of our approach over the traditional image-by-image training approach.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.468-476
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• 2002

Digital images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in medical radio-fluoroscopy images is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. We proposed adaptive weighed median(AWM) filters based on local statistics. We showed two ways of realizing the AWM filters. One is a simple type of AWM filter, which is constructed by Homogeneous factor(HF). Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by Visual C++ language on a IBM-PC Pentium 550 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of NMSE(normalized mean square error) with the value of the other existing filtering methods.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.487-501
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• 2017

A machine vision system (MVS) is a computer system that utilizes one or more image-capturing devices to provide image data for analysis and interpretation. Recently there have been a number of industrial- and medical-device applications where control charts have been proposed for use with image data. The use of image-based control charting is somewhat different from traditional control charting applications, and these differences can be attributed to several factors, such as the type of data monitored and how the control charts are applied. In this paper, we investigate the adjustment effect of image size and region of interest (ROI) size, when we use control charts to monitor grayscale image data in industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Progress in Medical Physics
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• v.15 no.2
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• pp.112-119
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• 2004

We tried to build human models based on medical images of live Korean, instead of using standard data of human body structures. Characteristics of mechanical structures of human bodies were obtained from medical images such as CT and MR images. For each constitutional part of mechanical structures CT images were analyzed in terms of gray levels and MR images were analyzed in terms of pulse sequence. Characteristic features of various mechanical structures were extracted from the analyses. Based on the characteristics of each structuring element we peformed image segmentation on CT and MR images. We delineated bones, muscles, ligaments and tendons from CT and MR images using image segmentation or manual drawing. For the image segmentation we compared the edge detection method, region growing method and intensity threshold method and applied an optimal compound of these methods for the best segmentation results. Segmented mechanical structures of the head/neck part were three dimensionally reconstructed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.163-166
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• 2006

The field of medical images has been digitalized as the development of computer and the digitalization of the medical instruments. As a result it causes a lot of problems such as an illegal copy related to medical images and property right of the medical images. Therefore, digital watermarking is used for discrimination whether the data are modified or not. It is also used to protect both the property right of medical images and the private life of many patients. The proposed theories, the Non-blind and the Blind method, have two problems. One is needed an original image and the other is using a gaussian watermarking. This paper proposes the new Blind Watermarking using binary images in order to easily recognize the results of watermark. This algorithm is described that an watermark of a binary image is wavelet-transformed, and then a transformed watermark is inserted in medium-band of frequency domains of original image by the Circular Input method. The propose method presented the good performance of over 0.97 in NC.

• Progress in Medical Physics
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• v.14 no.2
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• pp.124-130
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• 2003

Currently, as a consequence of PACS (Picture Archiving Communication System) implementation many hospitals are replacing conventional film-type interpretations of diagnostic medical images with new digital-format interpretations that can also be saved, and retrieve However, the big limitation in PACS is considered to be the lack of mobility. The purpose of this study is to determine the optimal communication packet size. This was done by considering the terms occurred in the wireless communication. After encoding medical image using JPGE2000 image compression method, This method embodied auto-error correction technique preventing the loss of packets occurred during wireless communication. A PC class server, with capabilities to load, collect data, save images, and connect with other network, was installed. Image data were compressed using JPEG2000 algorithm which supports the capability of high energy density and compression ratio, to communicate through a wireless network. Image data were also transmitted in block units coeded by JPEG2000 to prevent the loss of the packets in a wireless network. When JPGE2000 image data were decoded in a PUA (Personal Digital Assistant), it was instantaneous for a MR (Magnetic Resonance) head image of 256${\times}$256 pixels, while it took approximately 5 seconds to decode a CR (Computed Radiography) chest image of 800${\times}$790 pixels. In the transmission of the image data using a CDMA 1X module (Code-Division Multiple Access 1st Generation), 256 byte/sec was considered a stable transmission rate, but packets were lost in the intervals at the transmission rate of 1Kbyte/sec. However, even with a transmission rate above 1 Kbyte/sec, packets were not lost in wireless LAN. Current PACS are not compatible with wireless networks. because it does not have an interface between wired and wireless. Thus, the mobile JPEG2000 image viewing system was developed in order to complement mobility-a limitation in PACS. Moreover, the weak-connections of the wireless network was enhanced by re-transmitting image data within a limitations The results of this study are expected to play an interface role between the current wired-networks PACS and the mobile devices.