• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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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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• v.14 no.3
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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.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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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 Biomedical Engineering Research
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• v.30 no.1
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• pp.34-40
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• 2009

Transforms including translation and rotation are required for registering two or more images. In medical applications, different registration methods have been applied depending on the structures: for rigid bodies such as bone structures, affine transformation was widely used. In most previous research, a single transform was used for registering the whole images, which resulted in low registration accuracy especially when the degree of deformation was high between two images. In this paper, a novel registration method is introduced which is based image sub-division and bilinear interpolation of transformations. The proposed method enhanced the registration accuracy by 40% comparing with Trimmed ICP for registering color and MRI images.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.392-401
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• 2006

Image Guided Surgery (IGS) system which has variously tried in medical engineering fields is able to give a surgeon objective information of operation process like decision making and surgical planning. This information is displayed through 3D images which are acquired from image modalities like CT and MRI for pre-operation. The technique of image registration is necessary to construct IGS system. Image registration means that 3D model and the object operated by a surgeon are matched on the common frame. Major techniques of registration in IGS system have been used by recognizing fiducial markers placed on the object. However, this method has been criticized due to additional trauma, its invasive protocol inserting fiducial markers in patient's bone and generating noise data when 2D slice images are acquired by image modality because many markers are made of metal. Therefore, this paper developed shape-based registration technique to improve the limitation of fiducial marker based IGS system. Iterative Closest Points (ICP) algorithm was used to match corresponding points and quaternion based rotation and translation transformation using closed form solution applied to find the optimized cost function of transformation. we assumed that this algorithm were used in Total Knee replacement (TKR) operation. Accordingly, we have developed region-based 3D registration technique based on anatomical landmarks and this registration algorithm was evaluated in a femur model. It was found that region-based algorithm can improve the accuracy in 3D registration.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.103-105
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• 2002

Whole body stereotactic radiosurgery (WBSRS) technique is believed to be useful for the metastatic lesions as well as relatively small primary tumors in the trunk. Unlike stereotactic radiosurgery to intracranial lesion, inherent limitation on immobilization of whole body makes it difficult to achieve the reliable setup reproducibility. For this reason, it is essential to develop an objective and quantitative method of evaluating setup error for WBSRS. An evaluation technique using image registration has been developed for this purpose. Point pair image registrations with WBSRS frame coordinates were performed between two sets of CT images acquired before each treatment. Positional displacements could be determined by means of volumetric planning target volume (PTV) comparison between the reference and the registered image sets. Twenty eight sets of CT images from 19 WBSRS patients treated in Asan Medical Center have been analyzed by this method for determination of setup random error of each treatment. It is objective and clinically useful to analyze setup error quantitatively by image registration technique with WBSRS frame coordinates.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.9
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• pp.140-147
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• 2007

As the use of robots in surgeries becomes more frequent, the registration of medical devices based on images becomes more important. This paper presents two numerical algorithms for the registration of cross-sectional medical images such as CT (Computerized Tomography) or MRI (Magnetic Resonance Imaging) by using the geometrical information from helix or line fiducials. Both registration algorithms are designed to be used for a surgical robot that works inside a cavity of human body. This paper also reports details about the fiducial pattern that includes four helices and one line. The algorithms and the fiducial pattern were tested in various computer-simulated situations, and the results showed excellent overall registration accuracy.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.553-560
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• 2008

The amount of image data used in medical institution is increasing rapidly with great development of medical technology. Therefore, an automation method that use image processing description, rather than manual macrography of doctors, is required for the analysis large medical data. Specially, medical image registration, which is the process of finding the spatial transform that maps points from one image to the corresponding points in another image, and 3D analysis and visualization skills for a series of 2D images are essential technologies. However, a high establishment cost raise a budget problem, and hence small scaled hospitals hesitate importing these medical visualizing system. In this paper, we propose a visualization system which allows user to manage datasets and manipulates medical images registration using an open source graphics tool - VTK(Visualization Tool Kit). The propose of our research is to get more accurate 3D diagnosis system in less expensive price, compared to existing systems.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.172-180
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• 2008

PET/CT fused image with anatomical and functional information have improved medical diagnosis and interpretation. This fusion has resulted in more precise localization and characterization of sites of radio-tracer uptake. However, a motion during whole-body imaging has been recognized as a source of image quality degradation and reduced the quantitative accuracy of PET/CT study. The respiratory motion problem is more challenging in combined PET/CT imaging. In combined PET/CT, CT is used to localize tumors and to correct for attenuation in the PET images. An accurate spatial registration of PET and CT image sets is a prerequisite for accurate diagnosis and SUV measurement. Correcting for the spatial mismatch caused by motion represents a particular challenge for the requisite registration accuracy as a result of differences in PET/CT image. This paper provides a brief summary of the materials and methods involved in multiple investigations of the correction for respiratory motion in PET/CT imaging, with the goal of improving image quality and quantitative accuracy.