• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.991-999
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• 2013

Lower limbs deformity is a congenital disease and can also be occurred by an acquired factor. This paper suggests a new technique for surgical planning of Corrective Osteotomy for Lower Limbs (COLL) using 2D-3D medical image registration. Converting to a 3D modeling data of lower limb based on CT (computed tomography) scan, and divide it into femur, tibia and fibula; which composing the lower limb. By rearranging the model based on the biplane 2D images of X-ray data, a 3D upright bone structure was acquired. There are two ways to array the 3D data on the 2D image: Intensity-based registration and feature-based registration. Even though registering Intensity-based method takes more time, this method will provide more precise results, and will improve the accuracy of surgical planning.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.287-293
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• 2012

This paper presents an efficient image registration method by measuring the similarity, which is based on multi-dimensional intensity fluctuation. Multi-dimensional intensity which considers 4 directions of the image, is applied to reflect more properties in similarity decision. And an intensity fluctuation is also applied to measure comprehensively the similarity by considering a change in brightness between the adjacent pixels of image. The normalized cross-correlation(NCC) is calculated by considering an intensity fluctuation to each of 4 directions. The 5 correlation coefficients based on the NCC have been used to measure the registration, which are total NCC, the arithmetical mean and a simple product on the correlation coefficient of each direction and on the normalized correlation coefficient by the maximum NCC, respectively. The proposed method has been applied to the problem for registrating the 22 face images of 243*243 pixels and the 9 person images of 500*500 pixels, respectively. The experimental results show that the proposed method has a superior registration performance that appears the image properties well. Especially, the arithmetical mean on the correlation coefficient of each direction is the best registration measure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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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.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.1-6
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• 2009

We propose an intensity-based image registration method for medical images. The proposed registration is performed by the use of a new measure based on the entropy of conditional probabilities. To achieve the registration, we define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. And we conduct experiments with our method as well as existing methods based on the sum of squared differences (SSD), normalized correlation coefficient (NCC), normalized mutual information (NMI) criteria. We evaluate the precision of SSD-, NCC-, MI- and MCE-based measurements by comparing the registration obtained from the same modality magnetic resonance (MR) images and the different modality transformed MR/transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.97-105
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• 2018

Purpose : Proton Therapy using Bragg-peak, because it has distinct characteristics in providing maximum dosage for tumor and minimal dosage for normal tissue, a medical imaging system that can quantify changes in patient position or treatment area is of paramount importance to the treatment of protons. The purpose of this research is to evaluate the usefulness of the algorithm by comparing the image matching through the set-up and in-house code through the existing dips program by producing a Matlab-based in-house registration code to determine the error value between dips and DRR to evaluate the accuracy of the existing treatment. Materials and Methods : Thirteen patients with brain tumors and head and neck cancer who received proton therapy were included in this study and used the DIPS Program System (Version 2.4.3, IBA, Belgium) for image comparison and the Eclipse Proton Planning System (Version 13.7, Varian, USA) for patient treatment planning. For Validation of the Registration method, a test image was artificially rotated and moved to match the existing image, and the initial set up image of DIPS program of existing set up process was image-matched with plan DRR, and the error value was obtained, and the usefulness of the algorithm was evaluated. Results : When the test image was moved 0.5, 1, and 10 cm in the left and right directions, the average error was 0.018 cm. When the test image was rotated counterclockwise by 1 and $10^{\circ}$, the error was $0.0011^{\circ}$. When the initial images of four patients were imaged, the mean error was 0.056, 0.044, and 0.053 cm in the order of x, y, and z, and 0.190 and $0.206^{\circ}$ in the order of rotation and pitch. When the final images of 13 patients were imaged, the mean differences were 0.062, 0.085, and 0.074 cm in the order of x, y, and z, and 0.120 cm as the vector value. Rotation and pitch were 0.171 and $0.174^{\circ}$, respectively. Conclusion : The Matlab-based In-house Registration code produced through this study showed accurate Image matching based on Intensity as well as the simple image as well as anatomical structure. Also, the Set-up error through the DIPS program of the existing treatment method showed a very slight difference, confirming the accuracy of the proton therapy. Future development of additional programs and future Intensity-based Matlab In-house code research will be necessary for future clinical applications.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.623-632
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• 2009

In this paper, we propose a pulmonary nodule registration for the tracking of lung nodules in sequential CT scans. Our method consists of following five steps. First, a translational mismatch is corrected by aligning the center of optimal bounding volumes including each segmented lung. Second, coronal maximum intensity projection(MIP) images including a rib structure which has the highest intensity region in baseline and follow-up CT series are generated. Third, rigid transformations are optimized by normalized average density differences between coronal MIP images. Forth, corresponding nodule candidates are defined by Euclidean distance measure after rigid registration. Finally, template matching is performed between the nodule template in baseline CT image and the search volume in follow-up CT image for the nodule matching. To evaluate the result of our method, we performed the visual inspection, accuracy and processing time. The experimental results show that nodules in serial CT scans can be rapidly and correctly registered by coronal MIP-based rigid registration and local template matching.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.191-194
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• 2003

In this paper, we propose a new accurate and robust image mosaic technique of the mural underdrawing taken from the infra-red camera, which is based on multiple image registration and adaptive blending technique. The image mosaicing methods which have been developed so far have the following deficits. It is hard to generate a high resolution image when there are regions that do not have features or intensity gradients, and there is a trade-off in overlapping region site in view of registration and blending. We consider these issues as follows. First, in order to mosaic Images with neither noticeable features nor intensity gradients, we use a Projected supplementary pattern and pseudo color image for features in the image Pieces which are registered. Second, we search the overlapping region size with minimum blending error between two adjacent images and then apply blending technique to minimum error overlapping region. Finally, we could find our proposed method is more effective and efficient for image mosaicing than conventional mosaic techniques and also is more adequate for the application of infra-red mural underdrawing mosaicing. Experimental results show the accuracy and robustness of the algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.1-9
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• 2009

Using the projected 2-dimensional tilted images obtained from the transmission electron microscopy, we can reconstruct the 3-dimensional structures of objects, such as cells. As a preprocessing procedure, the tilted images should be registered and compensated in terms of the spatial position and the intensity difference, respectively. In this paper, we employ the fiducial marker-based approach to perform a registration, and introduce a simple intensity compensation scheme. Based on the transmissivity image formation model, we propose an algorithm that can compensate the components of the illumination and transmissivity of each image according to the tilted angle. Numerical analysis using real images obtained from the transmission electron microscopy are shown in this paper to show the performance of the proposed algorithm.