• Korean Journal of Computational Design and Engineering
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• v.15 no.3
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• pp.234-242
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• 2010

3D CAD technology has been extended to a medical area including dental clinic beyond industrial design. The 2D images obtained by CT(Computerized Tomography) and MRI(Magnetic Resonance Imaging) are not intuitive, and thus the volume rendering technique, which transforms 2D data into 3D anatomic information, has been in practical use. This paper has focused on a method and its implementation for forming 3D geometric surface model from laminated CT images of the pubis. The implemented system could support a dental clinic to observe and examine the status of a patient's pubis before implant surgery. The supplement of 3D implant model would help dental surgeons settle operation plans more safely and confidently. It also would be utilized with teaching materials for a practice and training.

• Smart Media Journal
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• v.5 no.1
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• pp.49-54
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• 2016

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.143-149
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• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• 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 Korean Society of Radiology
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• v.2 no.4
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• pp.41-46
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• 2008

Computed tomography (CT) is a medical imaging method employing tomography. CT is a 3-Dimensional (3-D) radiographic imaging technique, which is not suited for assessment of inflammation, but can be considered a reference method for assessment of bone damage, due to its direct 3-D visualization of calcified tissue. In this study of pathological joint changes in a rat model of adjuvant-induced arthritis (AIA) and quality analysis of bone destructions were performed by 3-Dimensional computed tomography images. These data demonstrate that the destructive progression of disease in a rat AIA model can be quantified using 3-D CT image analysis, which allows assessment of arthritic disease status and efficacy of experimental therapeutic agents.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.1
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• pp.1-10
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• 2010

Purpose: The effect of artifact was analyzed, which occurs from fiducial marker during the liver Image Guided Radiation Therapy (IGRT) using the fiducial marker. Materials and Methods: The size of artifact of fixed fiducial marker and length of mobile fiducial marker locus were measured using the On-Board Imager system (OBI) and CT simulator, and 2D-2D matching and 3D-3D matching were carried out, respectively, and at this time, the coordinates transition value of couch was analyzed. Results: The measurement of fixed fiducial marker artifact size indicated CT 4.90, 8.10, 12.90, 19.70 mm and OBI 5.60, 10.60, 14.70, 29.40 mm based on the reference CT slice thickness of 1.25, 2.50, 5.00, and 10.00 mm. Meanwhile, the measurement of mobile fiducial marker locus length indicated CT 42.00, 43.10, 46.50 mm, and OBI 43.40, 46.00, 49.30 mm. The coordinates transition of 1.00, 2.00, and 8.00 mm occurred between 2D-2D matching and 3D-3D matching. Conclusion: It was confirmed that the therapy error increased during IGRT due to the influence of artifact when CT slice thickness increased. Thus, it may be desirable to acquire the image less than 2.50 mm in slice thickness when IGRT is implemented using the fiducial marker.

• Korean Journal of Head & Neck Oncology
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• v.26 no.2
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• pp.193-197
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• 2010

Objectives : Patients with neck tumor and their family need every information about the disease. Especially, the size and location are confusing with verbal information. With the aid of CT, the problem had some answer, but it needs some medical education. We would like to know the usefullness of 3D reconstructed images in patient education about the disease. Material and Methods : Neck CT data were collected from 10 patients with various neck tumors and converted to 3D reconstructed images. Understanding of the patients about the size and location of tumors were rated from questionaires using axial CT images and 3D images. Results : Understanding score about 3D images were greater than that of CT images(p<0.006). Conclusion : 3D reconstructed images of CT could give the patients more real visual information about the disease.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.83-92
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• 2011

The random and heterogeneous pore structure is a significant factor that dominates physical and mechanical behaviors of soils such as fluid flow and geomechanical responses driven by loading. The characterization method using non-destructive testing such as micro X-ray CT technique which has a high resolution with micrometer unit allows to observe internal structure of soils. However, the application has been limited to qualitatively observe 2D and 3D CT images and to obtain the void ratio at macro-scale although the CT images contain enormous information of materials of interests. In this study, we constructed the 3D particle and pore structures based on sequentially taken 2D images of glass beads and quantitatively defined complex pore structure with void cell and void channel. This approach was enabled by implementing image processing techniques that include coordinate transformation, binarization, Delaunay Triangulation, and Euclidean Distance Transform. It was confirmed that the suggested algorithm allows to quantitatively evaluate the distribution of void cells and their connectivity of heterogeneous pore structures for glass beads.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.757-768
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• 2022

In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.16-21
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• 2016

Computed tomography (CT) can completely digitize the interior and the exterior of nearly any object without any destruction. Generally, the resolution for industrial CT is below a few microns. The industrial CT scanning, however, has a limitation because it requires long measuring and processing time. Whereas, 2D X-ray imaging is fast. In this paper, we propose a novel concept of 3D non-destructive inspection technique using the advantages of both micro-CT and dual X-ray images. After registering the master object’s CT data and the sample objects’ dual X-ray images, 3D non-destructive inspection is possible by analyzing the matching results. Calculation for the registration is accelerated by parallel computing using graphics processing unit (GPU).