• Journal of information and communication convergence engineering
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• 제8권6호
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• pp.690-696
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• 2010

Web-based virtual collaboration is increasingly gaining popularity in almost every area in our society due to the fact that it can bridge the gap imposed by time and geographical constraints. However, in medical field, such collaboration has been less popular than other fields. Some of the reasons were timeliness, security, and preciseness of the information they are dealing with. In this paper, we are proposing a web-based distributed medical collaboration system called Virtual Collaboration System for Medicine (VCSM) for medical doctors that meet the needs. The proposed system consists of two parts - multimedia presentation and recordable virtual collaboration. The former supports synchronized multimedia presentation using Synchronous Multimedia Integration Language (SMIL.) It allows synchronization of the contents of a PowerPoint presentation file and a video file. The presentation may be provided to the participants before the discussion begins. Next, in the virtual collaboration stage, participants can use text along with associated symbols during the discussion over the presented medical images. The symbols such as arrows or polygons can be set or removed dynamically to represent areas of interest in digital images using so called layered architecture that separates image layer from annotation layer. XML files are used to record participants' opinions along with the symbols over some particular images

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.338-344
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• 2021

The recently continuous enhancement and development in the biomedical side for the betterment of human life. The Wireless Body Area Networks is a significant tool for the current researcher to design and transfer data with greater data rates among the sensors and sensor nodes for biomedical applications. The core area for research in WBANs is power efficiency, battery-driven devices for health and medical, the Charging limitation is a major and serious problem for the WBANs.this research work is proposed to find out the optimal solution for battery-friendly technology. In this research we have addressed the solution to increasing the battery lifetime with variable data transmission rates from medical equipment as Wireless Endoscopy Capsules, this device will analyze a patient's inner body gastrointestinal tract by capturing images and visualization at the workstation. The second major issue is that the Wireless Endoscopy Capsule based systems are currently not used for clinical applications due to their low data rate as well as low resolution and limited battery lifetime, in case of these devices are more enhanced in these cases it will be the best solution for the medical applications. The main objective of this research is to power optimization by reducing the power consumption of the battery in the Wireless Endoscopy Capsule to make it battery-friendly. To overcome the problem we have proposed the algorithm for "Battery Friendly Algorithm" and we have compared the different frame rates of buffer sizes for Transmissions. The proposed Battery Friendly Algorithm is to send the images on average frame rate instead of transmitting the images on maximum or minimum frame rates. The proposed algorithm extends the battery lifetime in comparison with the previous baseline proposed algorithm as well as increased the battery lifetime of the capsule.

• 정보보호학회논문지
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• 제21권6호
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• pp.13-20
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• 2011

오늘날 디지털 이미지의 사용범위는 웹, 산업, 의료분야 까지 많은 분야에서 사용되고 있다. 웹과 온라인 저작권의 경우 많은 연구가 진행되어왔지만, 산업용 디지털 이미지 보안에 관한 연구는 미비한 실정이다. 본 논문에서는 산업용 필름에서 사용되는 디지털 이미지 암호화 기법을 제안한다. 산업용 지적 재산권 및 산업기밀 유출을 막기 위한 산업용 이미지 보안에 적합한 이미지 암호화 기법을 구현 및 검증한다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• 한국정보통신학회논문지
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• 제21권4호
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• pp.759-766
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• 2017

제안 논문에서는 의료영상의 다양한 공격에 안전하고 견고하도록 의료 영상을 일정기간 압축 저장해 디지털 콘텐츠에 투명성을 유지한다. 제안논문은 오리지널 영상의 특징 값을 추출하고 사용자의 정보와 결합해서 암호화된 영상 인증코드를 생성한다. 인증코드는 은닉된 데이터를 추출하기 위해 먼저 암호화된 의료영상을 복호화하고 영상의 공간 특성을 이용해 은닉데이터를 추출한다. 따라서 워터마킹을 통해 콘텐츠 자체에 직접 삽입 후 영상의 인증을 위해 추출된 인증코드와 새로 생성된 인증코드의 비교를 수행해 무결성을 보장하고 영상자료의 다양한 공격 안전함을 증명하고 인증률도 98.4%로 향상됨을 증명하였다.

• 대한의용생체공학회:의공학회지
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• 제23권2호
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• pp.81-85
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• 2002

본 논문에서는 의료 영상에 대한 새로운 비견고 워터마킹 알고리듬을 제안한다. 이 알고리듬은 의료 영상의 보안 및 위조 문제를 해결 할 수 있다. 제안한 알고리듬에서는 영상의 고유한 특성을 나타내는 특이점 (singularity)을 추출하여 이를 워터마크로 사용한다. 이때 특이점 추출에는 영상의 에지 성분을 정확하게 추출하는 장점을 갖는 Mallat 웨이브릿 변환을 이용한다. 즉 Mallat 웨이브릿 변환을 통해 생성된 첫 번째 스케일의 수평 및 수직 상세 부대역 (detail subband)을 이용하여 상세 신호에 대한 크기 성분 및 위상 성분을 계산한 후, 이러한 정보들을 이용하여 입력 영상의 국부 계수 최대치 (local modulus maxima, LMM)를 추출한다. LMM은 영상의 고유한 특성을 갖는 특이전을 나타내므로, 어떠한 조작이 가해진 영상의 LMM은 원 영상의 LMM과의 차이를 나타낸다. 따라서 임의의 영상에 대하여 LMM을 구한 후 원 영상의 LMM과 비교함으로써 위조 여부를 판단할 수 있다. 제안한 알고리듬의 성능 평가를 위한 모의 실험을 통하여 제안한 워터마킹 알고리듬은 의료 영상의 위조된 부분을 정확하게 추출하는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• 한국인공지능학회지
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• 제9권2호
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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.

• 한국정보통신학회논문지
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• 제25권8호
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• pp.1046-1052
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• 2021

4차 산업의 발전과 고성능의 컴퓨팅 환경 구축으로 다양한 산업분야에서 인공지능이 적용되고 있다. 의료분야에서는 X-Ray, MRI, PET 등의 의료 영상 및 임상 자료를 이용하여 암, COVID-19, 골 연령 측정 등의 딥 러닝 학습이 진행되었다. 또한 스마트 의료기기, IoT 디바이스와 딥 러닝 알고리즘을 적용하여 ICT 의료 융합 기술 등이 연구되고 있다. 이러한 기술 중 의료 영상 기반 딥 러닝 학습은 의료 영상의 바이오마커를 정확히 찾아내고, 최소한의 손실률과 높은 정확도가 필요하다. 따라서 본 논문은 흉부 X-Ray 이미지 기반 딥 러닝 학습 과정에서 손실률을 도출하는 손실 함수 중 영상분류 알고리즘에서 사용되는 Cross-Entropy 함수들의 성능을 비교·분석하고자 한다.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1150-1161
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• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.