• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.311-317
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• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.279-287
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• 2009

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality providing cross-sectional images of a conductivity distribution inside an electrically conducting object. MREIT has rapidly progressed in its theory, algorithm and experimental technique and now reached the stage of in vivo animal and human experiments. Conductivity image reconstructions in MREIT require various steps of carefully implemented numerical computations. To facilitate MREIT research, there is a pressing need for an MREIT software package with an efficient user interface. In this paper, we present an example of such a software, called CoReHA which stands for conductivity reconstructor using harmonic algorithms. It offers various computational tools including preprocessing of MREIT data, identification of boundary geometry, electrode modeling, meshing and implementation of the finite element method. Conductivity image reconstruction methods based on the harmonic $B_z$ algorithm are used to produce cross-sectional conductivity images. After summarizing basics of MREIT theory and experimental method, we describe technical details of each data processing task for conductivity image reconstructions. We pay attention to pitfalls and cautions in their numerical implementations. The presented software will be useful to researchers in the field of MREIT for simulation as well as experimental studies.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• 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.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2004.05a
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• pp.156-160
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• 2004

Our time is called as the era of information-oriented society and up-to-date scientific technology, but it can be also called as the era of image. It is because the image has much influence upon our everyday life. In the deluge of images, the delivery of image Is a better and faster synergic tool than the delivery of language, and this trend is consistent with the phases of the times in which the analog modality of synergy is converted into the digital one. The image is a picture that is formed by a physical process, and is all the figures reflected by electronic equipments that reproduce the image. After the discovery of photograph in 1893, the image has been appeared first with the form of movie, and then with the form of TV. However, the appearance of such unidirectional synergic tools as movies and TV. has provided artists with some creative motives with which they intercommunicate their messages via some experimental bidirectional synergic tools. Among image-media arts, the performance of installation and participation is an important example that causes many changes through conceptional conversion, such as the conception of the time in space and formative characteristics. What satisfy this trend have been videos, lasers, holographic, and etc. Furthermore, the appearance of computer has provided a synesthesia, that is, a virtual reality, and mixed a real image with a visional one. The image-media art will realize better synergy through combining brand new machines, informational technology, and art, and have much influence upon our everyday life formatively and artistically in the future. with this study, through analyzing the meaning of relationship between image equipments and space.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2349-2351
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• 2010

Photo Acoustic Tomography (PAT) is a hybrid imaging modality which combines high contrast of optical imaging and spatial resolution of ultrasound imaging, thus it is suitable to image biological tissue noninvasively. Laser-induced photoacoustic signals were measured from a sample by means of an unfocused ultrasound transducer, then PAT image was reconstructed based on a universal back-projection algorithm. To evaluate the feasibility of our system, phantom test was performed, consequently, the PAT images obtained using our system showed highly analogous shape and volume with those of the phantom. This result demonstrated that our system can provide a powerful tool for imaging the substructure of biological tissue in non-invasive manner.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1394-1397
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• 1987

It is difficult to detect edge segments from a noisy image since the image have a noise in piratical applications which utilize some type of visual input capability. Hence, the proposed algorithm consists of the modality tests based on parallel statistical tests without a noise removal preprocessing or postprocessing, and the edge detection technique With one-Pixel edge segments in this paper. The algorithm is very reliable and effective in the case of those situations where the Picture is poor quality and low resolution. And it does'nt require thinning operation and thresholding in hand. Experimental comparision With the more conventional techniques when applied to typical low-quality Pictures confirms good capabilities of the algorithm.

• The Journal of the Korean dental association
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• v.57 no.11
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• pp.700-707
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• 2019

On account of the dentistry mainly deals with hard tissues such as teeth and bones, CBCT and CT are common imaging modality in clinics. Recently, insurance coverage of MRI has been extended to head and neck region. In addition, unlike CBCT or CT, ionizing radiation is not used, the demand for MRI is gradually increasing in dentistry. Even though, many dentists still think that MRI is a difficult diagnostic tool and they are not confident when to use it. Therefore, the objective of this manuscript is to help the dental clinicians to access easily on MRI by introducing the overview, application and precautions of this imaging tool.