• Title/Summary/Keyword: Image convergence

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Feature extraction of medical image using GLCM (GLCM을 이용한 의료영상 특징정보 추출)

  • Park, Yong Sung;Jeong, Su Young;Kim, Wook;Lim, Ilhan;Kang, Joo Hyun;Lim, Sang Moo;Woo, Sang-Keun
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2017.01a
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    • pp.239-240
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    • 2017
  • 본 연구는 의료영상내 특징정보를 추출 및 평가함으로서 정밀의료 실현 가능성을 확인하고자 하였다. 영상화는 PET/CT 및 MRI 스캐너를 이용하여 암환자의 기능적 정보와 해부학적 정보를 획득하고 관심영역을 설정하였으며 각각의 영상내 특징정보를 추출하였다. 영상내 특징정보는 GLCM을 이용하여 에너지, 대비, 엔트로피, 균질성을 획득하였고, 획득된 영상 데이터에 따른 관심영역 설정 차이를 확인하였다. 영상내 특징 정보는 MRI 영상의 해부학적 정보를 이용한 분석결과에서 엔트로피 및 균질성이 PET 보다 증가 하였고 대비는 감소함을 확인하였다. 추후연구는 다양한 영상내 특징 정보를 획득하고 정밀의료를 위한 기계학습에 활용할 예정이다.

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Development and Performance Analysis of a Cultural Heritage Search Application Utilizing Image Recognition (이미지 인식을 활용한 문화유산 검색 어플리케이션 개발)

  • Hyun-Ji Kim;Tae-Hyun Shin;Hyun-Bin Jeong;Da-Hyun Kim;Jai-Soon Baek;Yong-Han Yu;Sung-Jin Kim
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2024.01a
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    • pp.181-183
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    • 2024
  • 본 논문은 이미지 인식, 지도 기반 검색, 그리고 키워드 검색을 활용한 문화유산 검색 어플리케이션의 개발과 성능 분석에 대한 연구를 다룬다. 우리는 이러한 다양한 기술과 기능을 결합하여 사용자에게 맞춤형 문화유산 정보를 제공하는 어플리케이션을 설계하고 구현하였다. 더불어, 어플리케이션의 성능을 평가하고 향상시키기 위한 실험과 분석을 수행하였다. 연구 결과, 이미지 인식 및 지도 기반 검색을 활용한 어플리케이션은 문화유산 관련 정보를 빠르고 정확하게 제공함으로써 사용자의 경험을 향상시킬 수 있음을 확인하였다. 이러한 연구는 문화유산 검색 어플리케이션의 개발과 성능 향상을 위한 중요한 기여를 제공할 것으로 기대된다.

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Region-Growing Segmentation Algorithm for Rossless Image Compression to High-Resolution Medical Image (영역 성장 분할 기법을 이용한 무손실 영상 압축)

  • 박정선;김길중;전계록
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.33-40
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    • 2002
  • In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

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Construction of Medical Image Information Viewer-Matching System Based by Diseases (질환별 의료영상정보 뷰어 매칭 시스템의 구축)

  • No, Si-Hyung;Ham, Gyu-Sung;Jeong, Chang-Won;Joo, Su-Chong
    • Journal of Internet Computing and Services
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    • v.20 no.5
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    • pp.37-47
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    • 2019
  • The purpose of this paper is to construct a system that matches the patient's image disease information with the medical image viewer in providing the medical image information to the medical staff. Currently, medical image information systems that are commercialized mostly provide only one image viewer with various image information of diseases or use incompatible exclusive viewers. For this reason, we designed and implemented a medical image information viewer matching system that integrates and provides specialized viewers that can be selected by diseases' image information. That is, it is a system to match and view medical image viewers based on disease information extracted from tag information stored as the metadata in DICOM file, which is medical image information standard, for disease-specific viewer matching. We analyzed the execution performances through our retrieval service of medical image information from our implementation system, and showed compatibility and control with various viewers.

Effects of Brand Image, Model Image and Context of Advertising Copy on Cosmetic Advertising (브랜드 이미지와 모델이미지 및 광고카피의 맥락이 화장품 광고효과에 미치는 영향)

  • Young-Jun Yeo
    • Journal of Advanced Technology Convergence
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    • v.2 no.3
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    • pp.49-58
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    • 2023
  • This study tried to verify the context effect in cosmetics advertisements by examining the cosmetics advertisement effect according to whether the brand image and the model image matched, and whether the brand image and the advertisement copy were harmoniously perceived. To this end, data were collected using the brand value type (3) × advertisement copy type (3) factorial design. The results are as follows. First, as a result of confirming the advertising effect according to the matching of the cosmetic brand image and the model image, it was found that both the advertising attitude and purchase intention were significantly high when the model image and the brand image matched. Second, it was confirmed whether there was a difference in the advertisement effect according to whether the cosmetic brand image and copy type matched. As a result, consumers who perceived that the cosmetic brand image and copy type matched had significantly higher advertising attitudes and purchase intentions than consumers who perceived that the copy type did not match. It is expected that it will provide validity as to whether the copy strategy should be established by incorporating the context effect when setting up a copy strategy for cosmetics advertisements in the future.

Numerical Modeling and Experiment for Single Grid-Based Phase-Contrast X-Ray Imaging

  • Lim, Hyunwoo;Lee, Hunwoo;Cho, Hyosung;Seo, Changwoo;Lee, Sooyeul;Chae, Byunggyu
    • Progress in Medical Physics
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    • v.28 no.3
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    • pp.83-91
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    • 2017
  • In this work, we investigated the recently proposed phase-contrast x-ray imaging (PCXI) technique, the so-called single grid-based PCXI, which has great simplicity and minimal requirements on the setup alignment. It allows for imaging of smaller features and variations in the examined sample than conventional attenuation-based x-ray imaging with lower x-ray dose. We performed a systematic simulation using a simulation platform developed by us to investigate the image characteristics. We also performed a preliminary PCXI experiment using an established a table-top setup to demonstrate the performance of the simulation platform. The system consists of an x-ray tube ($50kV_p$, 5 mAs), a focused-linear grid (200-lines/inch), and a flat-panel detector ($48-{\mu}m$ pixel size). According to our results, the simulated contrast of phase images was much enhanced, compared to that of the absorption images. The scattering length scale estimated for a given simulation condition was about 117 nm. It was very similar, at least qualitatively, to the experimental contrast, which demonstrates the performance of the simulation platform. We also found that the level of the phase gradient of oriented structures strongly depended on the orientation of the structure relative to that of linear grids.

Classification of 18F-Florbetaben Amyloid Brain PET Image using PCA-SVM

  • Cho, Kook;Kim, Woong-Gon;Kang, Hyeon;Yang, Gyung-Seung;Kim, Hyun-Woo;Jeong, Ji-Eun;Yoon, Hyun-Jin;Jeong, Young-Jin;Kang, Do-Young
    • Biomedical Science Letters
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    • v.25 no.1
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    • pp.99-106
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    • 2019
  • Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

Contents-based Image Retrieval using Fuzzy ART Neural Network (퍼지 ART 신경망을 이용한 내용기반 영상검색)

  • 박상성;이만희;장동식;김재연
    • Journal of the Institute of Convergence Signal Processing
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    • v.4 no.2
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    • pp.12-17
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    • 2003
  • This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

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A Development of a Collision Prevention System by a Moving Image (이동 영상에 의한 충돌 방지 시스템의 개발)

  • 박영식
    • Journal of the Institute of Convergence Signal Processing
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    • v.4 no.4
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    • pp.1-6
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    • 2003
  • In this Paper, the moving image is detected by a collision preventive system. The noise of these images is reduced by a mean filter. In case of detecting a movement with a binary difference image the moving area is detected exactly by the labeling and the projective method. When the image move slowly with the tracking mode of the system, the center of the tracking window move to the previous tracking window. And the tracking windows are divided into a tracking mode and a coasting mode which are determine by the Contrast-Difference Correlation of the date obtained from a difference image. The coasting mode determine whether continue the tracking step or not comparing the coasting-time values to reducing the error by the disturbance. The coasting and tracking of these moving images are verified by the result of the simulation.

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A Study on Authentication using Image Synthesis (이미지 합성을 이용한 인증에 대한 연구)

  • Kim, Suhee;Park, Bongjoo
    • Convergence Security Journal
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    • v.4 no.3
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    • pp.19-25
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    • 2004
  • This research develops an algorithm using image synthesis for a server to authenticate users and implements it. The server creates cards with random dots for users and distribute them to users. The server also manages information of the cards distributed to users. When there is an authentication request from a user, the server creates a server card based on information of the user' s card in real time and send it to the user. Different server card is generated for each authentication. Thus, the server card plays a role of one-time password challenge. The user overlaps his/her card with the server card and read an image(eg. a number with four digits) made up from them and inputs the image to the system. This is the authentication process. Keeping security level high, this paper proposes a technique to generate the image clearly and implements it.

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