• 대한의용생체공학회:의공학회지
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• 제18권3호
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• pp.223-231
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• 1997

본 논문에서는 3차원 의료 영상의 압축을 위한 인터프레임 부호화 방법을 제안한다. 슬라이스 사이의 변화를 뼈나 조직의 움직임으로 간주하여 움직임 보상 기법을 통해 이전 프레임으로부터 현재 프레임을 예측하고, 변환 부호화를 사용하여 오차 영상을 압축한다. 의료 영상의 슬라이스 사이의 복잡한 변화를 잘 예측하기 위해 동영상 부호화에서 가장 널리 사용되는 블럭 정합 알고리즘 (BMA) 대신 bilinear 변환을 통한 영상 warping을 사용하였다. 이 warping 방법은 슬라이스 사이에서 object가 없어지는 경우 예측 성능이 저하되는데, 이러한 단점을 보완하기 위해 블럭 겹침 움직임 보상 (OBMC) 기법을 결합하였다. 움직임 보상된 오차 영상의 부호화에는 EZW 부호화를 사용하였고, 이 때 각 프레임의 wavelet 계수의 양자화 오차를 동일하게 하여 프레임마다 일정한 화질을 얻도록 하였다. 모의 실험에서 warping을 사용한 인터프레임 부호화는 각 프레임을 독립적으로 부호화하는 방식보다 높은 압축 성능을 보였고, OBMC를 결합함으로써 warping만을 사용했을 때보다 성능이 더 개선되었다.

• 전기학회논문지
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• 제63권1호
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• pp.99-107
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• 2014

In this paper, a landmark based localization system using a Kinect sensor is proposed and evaluated with the implemented system for precise and autonomous navigation of low cost robots. The proposed localization method finds the positions of landmark on the image plane and the depth value using color and depth images. The coordinates transforms are defined using the depth value. Using coordinate transformation, the position in the image plane is transformed to the position in the body frame. The ranges between the landmarks and the Kinect sensor are the norm of the landmark positions in body frame. The Kinect sensor position is computed using the tri-lateral whose inputs are the ranges and the known landmark positions. In addition, a new matching method using the pin hole model is proposed to reduce the mismatch between depth and color images. Furthermore, a height error compensation method using the relationship between the body frame and real world coordinates is proposed to reduce the effect of wrong leveling. The error analysis are also given to find out the effect of focal length, principal point and depth value to the range. The experiments using 2D bar code with the implemented system show that the position with less than 3cm error is obtained in enclosed space($3,500mm{\times}3,000mm{\times}2,500mm$).

• 한국정보통신학회논문지
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• 제10권6호
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• pp.1153-1158
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• 2006

자궁 경부 세포진 영상의 핵 영역 분할은 자궁 경부암 자동화 검색 시스템의 가장 어렵고도 중요한 분야로 알려져 있다. 자궁 경부 세포진 영상은 배경과 세포의 영역이 확실히 구분되지 않는 경우가 많기 때문에 이들을 확실히 구분하는 것이 매우 중요하다. 본 논문에서는 이러한 문제점을 해결하기 위해 자궁 경부 세포진 영상에서 Region growing 기법을 적용하여 세포 영상을 분할한다. Region growing 기법은 화소간의 유사도를 측정하여 영역을 확장하여 분할하는 방법이다. 세포와 배경이 분할된 영상을 일정 임계값을 이용하여 영상을 이진화 한 후, 8방향 윤곽선 추적 알고리즘을 이용해 세포 영역을 추출한다. 추출된 세포 영역을 원 영상인 RGB 컬러로 변환한 후에 K-means 알고리즘을 적용하여 각 세포 영역의 RGB 화소를 R, G, B 채널로 각각 분리하여 클러스터링 한다. 클러스터링된 각 각의 R, G, B 채널의 클러스터 값을 이용하여 HSI 모델로 변환시킨 후에 세포핵 영역의 Hue 정보를 추출한다. 추출된 세포핵의 특징을 오류 역전파 알고리즘을 적용하여 정상 세포와 비정상 세포를 분류하고 인식한다.

• Journal of Korean Neurosurgical Society
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• 제30권2호
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• pp.186-193
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• 2001

Objectives : Trigeminal neuralgia and hemifacial spasm are caused by vascular compression of the REZ(root entry or exit zone) of the 5th and the 7th cranial nerve. Preoperative detection of neurovascular compression is essential for accurate diagnosis, appropriate treatment, and the good operative results. Three dimensional Fourier Transformation-Constructive Interference in Steady State(3DFT-CISS) images are known to give good contrast between CSF, nerve, and vessels. We applied a 3DFT-CISS imaging technique for the preoperative evaluation of patients with these diseases and estimated the diagnostic accuracy and usefulness of this study. Methods : A series of 71 patients with trigeminal neuralgia and hemifacial spasm were treated by microvascular decompression. Among them 34 patients with trigeminal neuralgia and 24 patients with hemifacial spasm had preoperative CISS images. We compared the radiologic finding with the operative finding, and analysed the diagnostic usefulness of 3DFT-CISS imaging. Results : The sensitivity of CISS images of detecting the neurovascular compression was 90.3% in trigeminal neuralgia and 100% in hemifacial spasm. There were one false-positive and three false-negative cases in trigeminal neuralgia, and one false-positive case in hemifacial spasm. The accuracy in diagnosing the causative vessel was 73.5% in trigeminal neuralgia and 83.3% in hemifacial spasm. Conclusion : CISS image is very useful diagnostic tool for preoperative evaluation of neurovascular compression in patients with trigeminal neuralgia and hemifacial spasm. No additional neuroradiologic examination other than CISS image and MRA is needed for preoperative evaluation of patients with trigeminal neuralgia and hemifacial spasm.

• 한국산학기술학회논문지
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• 제20권10호
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• pp.203-208
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• 2019

디지털 워터마킹은 디지털 컨텐츠에 대한 불법적인 복제를 방지하기 위한 저작권 보호 용도로 개발 되었으나, 최근에는 의료 영상과 같은 디지털 컨텐츠에 대하여 무결성을 검증하고 불법적인 조작이나 변형 위치를 감지하기 위한 기술로도 활용하고 있다. 디지털 컨텐츠에 대한 불법적인 복제를 방지하기 위한 저작권 보호 기술에서는 디지털 컨텐츠에 삽입된 워터마크가 왜곡이나 필터링과 같은 다양한 공격에 대하여 강인해야 하는 특성이 있어야 한다. 그러나 디지털 컨텐츠에 대한 조작이나 변형을 감지하기 위한 기술에서는 컨텐츠에 대한 사소한 변형에 대해서도 삽입된 워터마크가 쉽게 제거되어야 하는 특성이 있어야 컨텐츠에 대한 변형 여부를 확인할 수 있다. 따라서 본 논문에서는 디지털 영상에 대한 변형이나 조작 여부를 쉽게 확인하기 위한 워터마킹 기술을 제안한다. 제안 방법에서는 영상에 대한 변형 유, 무를 확인하기 위해 전체 영상을 $16{\times}16$ 블록 단위로 변형 여부를 검사하고 변형이 발생 된 블록에 대해서는 $4{\times}4$ 블록 단위로 검사를 수행하여 변형이 발생 된 위치를 확인한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• 대한의용생체공학회:의공학회지
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• 제44권5호
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• pp.303-314
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• 2023

This study aimed to assess the practical feasibility of advanced deformable image registration (DIR) algorithms in radiotherapy by employing two distinct datasets. The first dataset included 14 4D lung CT scans and 31 head and neck CT scans. In the 4D lung CT dataset, we employed the DIR algorithm to register organs at risk and tumors based on respiratory phases. The second dataset comprised pre-, mid-, and post-treatment CT images of the head and neck region, along with organ at risk and tumor delineations. These images underwent registration using the DIR algorithm, and Dice similarity coefficients (DSCs) were compared. In the 4D lung CT dataset, registration accuracy was evaluated for the spinal cord, lung, lung nodules, esophagus, and tumors. The average DSCs for the non-learning-based SyN and NiftyReg algorithms were 0.92±0.07 and 0.88±0.09, respectively. Deep learning methods, namely Voxelmorph, Cyclemorph, and Transmorph, achieved average DSCs of 0.90±0.07, 0.91±0.04, and 0.89±0.05, respectively. For the head and neck CT dataset, the average DSCs for SyN and NiftyReg were 0.82±0.04 and 0.79±0.05, respectively, while Voxelmorph, Cyclemorph, and Transmorph showed average DSCs of 0.80±0.08, 0.78±0.11, and 0.78±0.09, respectively. Additionally, the deep learning DIR algorithms demonstrated faster transformation times compared to other models, including commercial and conventional mathematical algorithms (Voxelmorph: 0.36 sec/images, Cyclemorph: 0.3 sec/images, Transmorph: 5.1 sec/images, SyN: 140 sec/images, NiftyReg: 40.2 sec/images). In conclusion, this study highlights the varying clinical applicability of deep learning-based DIR methods in different anatomical regions. While challenges were encountered in head and neck CT registrations, 4D lung CT registrations exhibited favorable results, indicating the potential for clinical implementation. Further research and development in DIR algorithms tailored to specific anatomical regions are warranted to improve the overall clinical utility of these methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• 대한원격탐사학회지
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• 제40권3호
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.