• 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 Information Processing Systems
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• v.11 no.4
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• pp.601-615
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• 2015

Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6919-6922
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• 2014

Background: The cytological analysis of serous effusions is a common investigation and yields important diagnostic information. However, the distinction of reactive mesothelial cells from malignant cells can sometimes be difficult for the cytopathologist. Hence cost-effective ancillary methods are essential to enhance the accuracy of cytological diagnosis. The aim of this study was to examine the utility of nuclear morphometry in differentiating reactive mesothelial cells from malignant cells in effusion smears. Materials and Methods: Sixty effusion smears consisting of 30 effusions cytologically classified as malignant (adenocarcinomas) and 30 benign effusions showing reactive mesothelial cells were included in the study. ImageJ was used to measure the nuclear area, perimeter, maximal feret diameter, minimal feret diameter and the circularity. A total of ten representative cells were studied in each case. Results: Significant differences were found between benign and malignant effusions for the nuclear area, perimeter, maximal feret diameter and minimal feret diameter. No significant difference was found for circularity, a shape descriptor. Receiver operating characteristic (ROC) curve analysis revealed that nuclear area, perimeter, maximal feret diameter, and minimal feret diameter are helpful in discriminating benign and malignant effusions. Conclusions: Computerised nuclear morphometry is a helpful ancillary technique to distinguish benign and malignant effusions. ImageJ is an excellent cost effective tool with potential diagnostic utility in effusion cytology.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.23-28
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• 2021

In this paper, we studied a system that can efficiently build security management for single-person households using Arduino, ESP32-CAM and PIR sensors, and proposed an Android app with an internet connection. The ESP32-CAM is an Arduino compatible board that supports both Wi-Fi, Bluetooth, and cameras using an ESP32-based processor. The PCB on-board antenna may be used independently, and the sensitivity may be expanded by separately connecting the external antenna. This system has implemented an Arduino-based Unauthorized intrusion system that can significantly help prevent crimes in single-person households using the combination of PIR sensors, Arduino devices, and smartphones. unauthorized intrusion system, showing the connection between Arduino Uno and ESP32-CAM and with smartphone applications. Recently, if daily quarantine is underway around us and it is necessary to verify the identity of visitors, it is expected that it will help maintain a safety net if this system is applied for the purpose of facial recognition and restricting some access. This technology is widely used to verify that the characters in the two images entered into the system are the same or to determine who the characters in the images are most similar to among those previously stored in the internal database. There is an advantage that it may be implemented in a low-power, low-cost environment through image recognition, comparison, feature point extraction, and comparison.

• Survey Research
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• v.12 no.3
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• pp.143-172
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• 2011

The relationship between body weight and body image, an objective and subjective measure of body shape, respectively, has long been a recurrent concern in the area of medical sociology and health-related studies. This concern stems from the argument and findings in the literature indicating that the two are not necessarily likely to be strongly correlated due mostly to the fact that one's own idea or conception about his/her body shape could be pretty different from one's actual shape. This study tries to empirically address the two issues based on the analysis of a national sample survey data in Korea: to what extent body weight and body image are correlated with or deviated from each other, on the one hand, and what factors help to account for the relationship between the two, on the other. The latest(2010) national sample data of KGSS(Korean General Social Survey) is used to evaluate the issues. Results of data analysis demonstrate that body weight and image have a moderate amount of correlation, and that the correlation tends to vary to a large extent depending on a few major socio-demographic and socio-economic characteristics. Most important, the risk factor analysis attempted in this study could identify several salient risk factors, which include gender, age, chronic diseases, smoking, physical exercises, and medical checkup. To be precise, those who may be best characterized as particularly risky to weight gains are females, who are in their 20's, who have chronic diseases, non-smokers, who exercise regularly, and who conduct medical checkups on a regular basis. To extrapolate, the findings suggest that the most typically risky kinds of individuals in Korea are "young women who care very much for their health." The findings are interpreted and discussed with suggesting a recommendation for further studies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.453-456
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• 2004

Due to the bandwidth and storage limitations medical images are needed to be compressed before transmission and storage. DICOM (Digital Imaging and Communications in Medicine) specification, which is the medical images standard, provides a mechanism for supporting the use of JPEG still image compression standard. In this paper, we explain a method for compressing medical images by PEG standard and propose two methods for JPEG compression. First, because medical images differ from natural images in optical feature, we propose a method to design adaptively the quantization table using spectrum analysis. Second, because medical images have higher pixel depth than natural images do, we propose a method to design Huffman table which considers the probability distribution feature of symbols. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Korean Journal of Digital Imaging in Medicine
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• v.15 no.2
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• pp.55-61
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• 2013

The purpose of this study is to evaluate image blurring according to variation of the ETL and propose the clinically appropriate ETL range. SIEMENS MAGNETOM Skyra 3.0T and 20 channel head coil were used for the study. MRI phantom was kept the lines horizontally to three direction(X,Y,Z) of the coil and T1, T2 weighted images that used the fast spin echo technique acquired. The ETL with increase of 10 was applied from 10 to 80. In addition, the ETL with increase of 1 was applied in the interval statistically significant differences occurred. And T1, T2 weighted images that used the conventional spin echo technique acquired to compare image blurring of the images that used the fast spin echo technique. The slope of lattice in the images was measured using Image J 1.47v program to evaluate image blurring. And image blurring was determined by the degree of the slope. The statistical significance of both techniques was evaluated by the Kruskal-Wallis test of the SPSS 17.0v. And the correlation of the ETL and image blurring was evaluated quantitatively by regression analysis. The slope of the T1, T2 weighted images that used fast spin echo technique decreased as contrasted with conventional spin echo technique. In the result of the Kruskal-Wallis test, the T1, T2 weighted images that used fast spin echo technique made a significant difference with conventional spin echo technique. Particularly, in the Tomhane' T2 test, the T1, T2 weighted images made a significant difference from ETL 22 and 31 respectively. In the result of the regression analysis, the R-squared of the T1, T2 weighted images are 0.762 and 0.793. It is difficult to apply the long ETL in the T1 weighted image caused by the short TR and multi-slices study. Therefore, clinical impact according to variation of the ETL is very slight in the T1 weighted images. But the application of the proper ETL is demanded in T2 weighted images using the fast spin echo technique in order to prevent image blurring.

• The Korean Journal of Cytopathology
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• v.7 no.1
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• pp.23-30
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• 1996

Chromatin texture, which partly reflects nuclear organization, is evolving as an important parameter indicating cell activation or transformation. In this study, chromatin pattern was evaluated by image analysis of the electron micrographs of follicular and papillary carcinoma cells of the thyroid gland and tested for discrimination of the two neoplasms. Digital grey images were converted from the electron micrographs, nuclear images, excluding nucleolus and intranuclear cytoplasmic inclusions, were obtained by segmentation; grey levels were standardized; and grey level histograms were generated. The histograms in follicular carcinoma showed Gaussian or near-Gaussian distribution and had a single peak, whereas those in papillary carcinoma had two peaks(bimodal), one at the black zone and the other at the white zone. In papillary carcinoma, the peak in the black zone represented an increased amount of heterochromatin particles and that at the white zone represented decreased electron density of euchromatin or nuclear matrix. These results indicate that the nuclei of follicular and papillary carcinoma cells differ in their chromatin pattern and the difference may be due to decondensed chromatin and/or matrix substances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.