• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.107-114
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• 2012

Microwave imaging (MI) is one of the most promising and attractive new techniques for earlier breast cancer detection. Microwave tomography (MT) realizes configuration of a multistatic multiple-input multiple-output system and reconstructs dielectric properties of the breast by solving a nonlinear inversion scattering problem. In this paper, we describe ETRI 3D MT system with 3D MI reconstruction program and demonstrate its robustness through some examples of the image reconstruction.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.115-121
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• 2014

Purpose: In addition to improving the quality of the PET image, through much research, the development of various programs are performed. Astonish TF reconstruction techniques by Philips can confirm the improved contrast of the lesion. Also, It's image reconstruction of 2 mm is possible with rapid reconstruction rate than conventional. In this study, we compared and evaluated Standardized Uptake Value (SUV) in accordance with the 2 mm reconstruction techniques and traditional 4 mm from the $^{18}F-FDG$ PET whole body image. Materials and Methods: In the phantom experiment, NEMA IEC body phantom (sphere: 10, 13, 17, 22, 28, 37 mm) was used to obtain images by using GEMINI TF 64 PET/CT (Philips, Cleveland, USA). Also, In the clinical images, we performed $^{18}F-FDG$ PET/CT examination to 30 women (age: $55.1{\pm}11.3$, BMI: $24.1{\pm}2.9$) with a diagnosis of breast cancer. After that, we reconstructed images in 2 mm and 4 mm respectively. The region of interest was drawn to acquired images. Since then, we measured SUV and statistically analyzed with SPSS ver.18 by using EBW (Extended Brilliance Workstation) NM ver.1.0. Results: After analyzing the result of the phantom study, there was a tendency that the bigger hot sphere size, the higher SUV. If you compared the 2 mm reconstruction techniques to 4 mm, it increased 95.78% in 10 mm, 50.60% in 13 mm, 25.00% in 17 mm, 30.04% in 22 mm, 31.81% in 28 mm, and 27.84% in 37 mm. Through the result of the analysis of the 2 mm reconstruction techniques and 4 mm in clinical images, it appeared that SUV of 2 mm was higher than that of 4 mm. Also the smaller the volume was, the more the change rate increased. Conclusion: After analyzing the result of the clinical picture and phantom experiments applied by Astonish TF reconstruction techniques, as the size of the volume was small, the change rate of the SUV increased. Therefore, it was necessary to further research about the SUV correction for accurate and active utilization of 2 mm reconstruction techniques which had excellent lesion discrimination ability and contrast in clinic.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.309-315
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• 2009

Image reconstruction of Inverse Fourier Transform after Frequency Domain Data is filtered applies to Image signal acquired from MR. There are various kinds of image processing techniques; image preprocessing, image reconstruction, image compression, image restoration image mixture, noise and artifact elimination, and image quality improvement. In this paper, optimum filter applicable to diagnosis in clinic by comparing and analyzing the characteristics of the filter will be explained. Fermi-Dirac filter will improve the image quality better than the previous MR image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1583-1590
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• 2011

As the existing radiation scanning systems use 2-dimensional radiation scanned images, the low accuracy has been pointed out as a problem of it. This research analyzes the applicability of the stereo image processing technique to X-ray scanned images. Two 2-dimensional radiation images which have different disparity values are acquired from a newly designed stereo image acquisition system which has one additional line sensor to the conventional system. Using a matching algorithm the 3D reconstruction process which find the correspondence between the images is progressed. As the radiation image is just a density information of the scanned object, the direct application of the general stereo image processing techniques to it is inefficient. To overcome this limitation of a stereo image processing in radiation area, we reconstruct 3-D shapes of the edges of the objects. Also, we proposed a new volume based 3D reconstruction algorithm. Experimental results show the proposed new volume based reconstruction technique can provide more efficient visualization for cargo inspection. The proposed technique can be used for such objects which CT or MRI cannot inspect due to restricted scan environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.75-80
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• 2017

Recently, there has been growing interest in spatial data that combines information and communication technology with smart cities. The high-precision LiDAR (Light Dectection and Ranging) equipment is mainly used to collect three-dimensional spatial data, and the acquired data is also used to model geographic features and to manage plant construction and cultural heritages which require precision. The LiDAR equipment can collect precise data, but also has limitations because they are expensive and take long time to collect data. On the other hand, in the field of computer vision, research is being conducted on the methods of acquiring image data and performing 3D reconstruction based on image data without expensive equipment. Thus, precise 3D spatial data can be constructed efficiently by collecting and processing image data using CCTVs which are installed as infrastructure facilities in smart cities. However, this method can have an accuracy problem compared to the existing equipment. In this study, experiments were conducted and the results were analyzed to increase the number of extracted matching points by applying the feature-based method and the area-based method in order to improve the precision of 3D spatial data built with image data acquired from stereo CCTVs. For techniques to extract matching points, SIFT algorithm and PATCH algorithm were used. If precise 3D reconstruction is possible using the image data from stereo CCTVs, it will be possible to collect 3D spatial data with low-cost equipment and to collect and build data in real time because image data can be easily acquired through the Web from smart-phones and drones.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.365-373
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• 1997

In this paper, a new method of the 3D(dimensional) image reconstruction is proposed to build up the 3D image from 2D images using digital image processing techniques and computer graphics. First, the new feature extraction algorithm that doesn't need various input parameters and is not affected by threshold is adopted This new algorithm extracts feature points by eliminating some undesirable points on the ground of the connectivity. Second, as the cast function to reconstruct surfaces using extracted feature points, the minimum distance measure between two plane images has been adopted According to this measure, the surface formation algorithm doesn't need complex calculation and takes the form of triangle or trapezoid To investigate usefulness, this approach has been applied to a head CT image and compared with other methods. Experimental comparisons show that the suggested algorithm yields better performance on feature extraction than others. In contrast with the other methods, the complex calculation for surface formation in the proposed algorithm is not necessary.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1588-1591
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• 2005

In this paper, we propose novel computational reconstruction technique of three-dimensional objects in integral imaging by use of a lenslet array. We applied our technique to two different integral imaging systems according the distance between lenslet array and elemental image plane. Experimental results are presented and discussed as well.

• Journal of The Korean Astronomical Society
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• v.47 no.2
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• pp.43-47
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• 2014

Many advanced ground-based solar telescopes improve the spatial resolution of observation images using an adaptive optics (AO) system. As any AO correction remains only partial, it is necessary to use post-processing image reconstruction techniques such as speckle masking or shift-and-add (SAA) to reconstruct a high-spatial-resolution image from atmospherically degraded solar images. In the New Vacuum Solar Telescope (NVST), the spatial resolution in solar images is improved by frame selection and SAA. In order to overcome the burden of massive speckle data processing, we investigate the possibility of using the speckle reconstruction program in a real-time application at the telescope site. The code has been written in the C programming language and optimized for parallel processing in a multi-processor environment. We analyze the scalability of the code to identify possible bottlenecks, and we conclude that the presented code is capable of being run in real-time reconstruction applications at NVST and future large aperture solar telescopes if care is taken that the multi-processor environment has low latencies between the computation nodes.

• Korean Journal of Optics and Photonics
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• v.19 no.4
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• pp.270-275
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• 2008

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.70-78
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• 2021

Estimating 3D information from a single image is one of the essential problems in numerous applications. Since a 2D image inherently might originate from an infinite number of different 3D scenes, thus 3D reconstruction from a single image is notoriously challenging. This challenge has been overcame by the advent of recent deep convolutional neural networks (CNNs), by modeling the mapping function between 2D image and 3D information. However, to train such deep CNNs, a massive training data is demanded, but such data is difficult to achieve or even impossible to build. Recent trends thus aim to present deep learning techniques that can be trained in a weakly-supervised manner, with a meta-data without relying on the ground-truth depth data. In this article, we introduce recent developments of weakly-supervised deep learning technique, especially categorized as scene 3D reconstruction and object 3D reconstruction, and discuss limitations and further directions.