• 반도체디스플레이기술학회지
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• 제18권2호
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• pp.63-66
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• 2019

Due to the development of medical imaging technology, different imaging technologies provide a large amount of effective information. However, different imaging method caused the limitations of information integrity by using single type of image. Combining different image together so that doctor can obtain the information from medical image comprehensively. Image registration algorithm based on mutual information has become one of the hotspots in the field of image registration with its high registration accuracy and wide applicability. Because the information theory-based registration technology is not dependent on the gray value difference of the image, and it is very suitable for multimodal medical image registration. However, the method based on mutual information has a robustness problem. The essential reason is that the mutual information itself is not have enough information between the pixel pairs, so that the mutual information is unstable during the registration process. A large number of local extreme values are generated, which finally cause mismatch. In order to overcome the shortages of mutual information registration method, this paper proposes a registration method combined with image spatial structure information and mutual information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• 대한원격탐사학회지
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• 제36권4호
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• 한국통신학회논문지
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• 제30권11C호
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• pp.1116-1124
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• 2005

We investigated the mean and variance of the MSE and the MI-based image registration methods that have been widely applied for image registration. By using the first order Taylor series expansion, we have approximated the mean and the variance for one-dimensional image registration. The asymptotic results show that the MSE based method is unbiased and efficient for the same image registration problem while the MI-based method shows larger variance. However, for the different modality image registration problem, the MSE based method is largely biased while the MI based method still achieves registration. The results imply that the MI based method achieves robustness to the different image modalities at the cost of inefficiency. The analytical results are supported by simulation results.

• International Journal of Internet, Broadcasting and Communication
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• 제7권1호
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• pp.36-41
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• 2015

This paper is proposed a wavelet-based MCDT(Mask Coefficient Differential and Threshold) method of image registration of Multi-images contaminated with visible image and infrared image. The method for ensure reliability of the image registration is to the increase statistical corelation as getting the common feature points between two images. The method of threshold the wavelet coefficients using derivatives of the wavelet coefficients of the detail subbands was proposed to effectively registration images with distortion. And it can define that the edge map. Particularly, in order to increase statistical corelation the method of the normalized mutual information. as similarity measure common feature between two images was selected. The proposed method is totally verified by comparing with the several other multi-image and the proposed image registration.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.102-104
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• 2019

Registering different kinds of clinical images widely used in diagnostic and surgery planning. However, cause of tumor growth or effected by gravity, human tissue has plenty of non-rigid deformation with clinically. Non-rigid registration allows the mapping of straight lines to curves. Therefore, such local deformation makes registration more complicated. In this work, we mainly introduce intra-subject, inter-modality registration. This paper mainly studies the nonlinear registration method of 2D medical image registration. The general medical image registration algorithm requires manual intervention, and cost long registration time. In our work to reduce the registration time in rough registration step, the barycenter and the direction of main axis of the image is calculated, which reduces the calculation amount compared with the method of using mutual information.

• Journal of International Society for Simulation Surgery
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• 제1권2호
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• pp.62-66
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• 2014

Image registration is the process for finding the correct geometrical transformation that brings one image in precise spatial correspondence with another image. There are limitations on the visualization of simple overlay between two different modality images because two different modality images have different anatomical information, resolution, and viewpoint. In this paper, various image registration methods and their applications are introduced. With the recent advance of medical imaging device, image registration is used actively in diagnosis support, treatment planning, surgery guidance and monitoring the disease progression.

• 핵의학기술
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• 제15권2호
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• pp.60-64
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• 2011

Brain의 질병을 평가하는 유용한 검사방법 중의 하나인 brain perfusion SPECT는 환자의 움직임으로 인한 검사의 실패확률이 높아 one day method를 사용하지 못하고 two days method를 사용해야 하는 경우가 많다. 본 연구에서는 image registration을 사용하여 검사의 실패확률을 줄이고 one day method로 검사를 시행할 수 있는지 image registration을 적용할 경우 검사의 신뢰성을 알아보고자 하였다. Jaszczak phantom에 준비된 방사성동위원소 $^{99m}Tc$을 insert에 111 MBq/mL가 되도록 분배하여 넣고 나머지 background에 3,145 MBq/mL가 되도록 넣어 1:8의 비율로 phantom을 제작하고 Hoffman 2-D brain phantom과 cylindrical uniform phantom에는 111 MBq/mL가 되도록 만든다. 완성된 phantom은 기본 위치에서 frame 당 5 sec씩 총 120 frame을 획득하여 영상을 얻었다. 또 Phantom과 환자의 데이터를 가지고 original 영상과 registration 영상, registration 시행한 후에 original 영상을 subtraction한 영상과 registration하지 않은 영상에서 subtraction한 영상 간의 임의의 같은 위치에 ROI를 설정하고 영상에서 counts 차이를 알아보았다. 실험 결과 약간의 counts 차이를 보였으나 이것은 실험시간이 경과함에 따른 RI의 decay와 phantom의 구조물이 없는 cylindlical phantom에서 조차 약간의 counts의 차이를 보이는 바로 미루어 봤을 때 실험 결과 나온 counts의 차이는 적다고 할 수 있을 것이다. 따라서 registration을 활용하여 brain perfusion SPECT의 단점들을 개선하고 정확한 진단에 도움을 줄 수 있을 것으로 사료된다.

• 대한원격탐사학회지
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• 제39권6_3호
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• pp.1707-1720
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• 2023

위성 시계열 데이터가 증가함에 따라 원격탐사 자료의 활용도가 높아지고 있다. 시계열 자료를 통한 분석에 있어 영상 간의 상대적인 위치 정확도는 결과에 큰 영향을 미치기 때문에 이를 보정하기 위한 영상 정합 과정은 필수적으로 선행되어야 한다. 최근에는 기존 알고리즘의 성능을 상회하는 딥러닝 기반 영상 정합 연구의 사례가 증가하고 있다. 딥러닝 기반 정합 모델을 학습하기 위해서는 수 많은 영상 쌍이 필요하다. 또한, 기존 딥러닝 모델의 데이터 간의 상관도 map을 제작하고, 이에 추가적인 연산을 적용하여 정합점을 추출는데 이는 비효율적이다. 이러한 문제를 해결하기 위해 본 연구에서는 영상 정합 모델 학습을 위한 데이터 증강 기법을 구축하여 데이터셋을 제작하였고, 이를 오프셋(offset) 양 자체를 예측하는 정합 모델인 OffsetNet에 적용하여 KOMSAT-2, -3, -3A 영상 정합을 수행하였다. 모델 학습 결과, OffsetNet은 평가 데이터에 대해 높은 정확도로 오프셋 양을 예측하였고, 이를 통해 주영상과 부영상을 효과적으로 정합하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.594-597
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• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a time-consuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.