• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.243-250
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• 1998

Reconstruction aspects of spiral scan imaging for ultra fast magnetic resonance imagine(MRI) have been investigated with polar and rectangular coordinates-based reconstruction. For the reconstruction of the spiral scan imaging, acquired data in spiral trjectory should be converted to polar or rectangular grids, where interpolation techniques are used. Various reconstruction algorithms for spiral scan imaging are tested, and reconstructed image qualities are compared with computed phantom. An improved reconstruction algorithm with dc-offset correction in projection domain is proposed, which provides the best reconstructed image quality from the simulation. Image artifact with existing algorithms is completely removed with the proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.1-6
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• 2015

Recently, handy pulse measurement method was introduced by using smart phone camera. However, measured values are not consistent with the variations of external light conditions, because the external light interfere with dynamic range of captured pulse image. Thus, adaptive dynamic range reconstruction algorithm is proposed to conduct pulse measurement robust to light condition. The minimum and maximum values for dynamic ranges of green and blue channels are adjusted to appropriate values for pulse measurement. In addition, sigmoid function based curve is applied to adjusted dynamic range. Experimental results show that the proposed algorithm conducts suitably dynamic range reconstruction of pulse image for the interference of external light sources.

• Progress in Medical Physics
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• v.27 no.3
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• pp.105-110
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• 2016

Sparse angular sampling has been studied recently owing to its potential to decrease the radiation exposure from computed tomography (CT). In this study, we investigated the analytic reconstruction algorithm in sparse angular sampling using the sinogram interpolation method for improving image quality and computation speed. A prototype of the spectral CT system, which has a 64-pixel Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,200 and 1,015 mm, respectively. Two energy bins (23~33 keV and 34~44 keV) were set to obtain two reconstruction images. We used a PMMA phantom with height and radius of 50.0 mm and 17.5 mm, respectively. The phantom contained iodine, gadolinium, calcification, and lipid. The Feld-kamp-Davis-Kress (FDK) with the sinogram interpolation method and Maximum Likelihood Expectation Maximization (MLEM) algorithm were used to reconstruct the images. We evaluated the signal-to-noise ratio (SNR) of the materials. The SNRs of iodine, calcification, and liquid lipid were increased by 167.03%, 157.93%, and 41.77%, respectively, with the 23~33 keV energy bin using the sinogram interpolation method. The SNRs of iodine, calcification, and liquid state lipid were also increased by 107.01%, 13.58%, and 27.39%, respectively, with the 34~44 keV energy bin using the sinogram interpolation method. Although the FDK algorithm with the sinogram interpolation did not produce better results than the MLEM algorithm, it did result in comparable image quality to that of the MLEM algorithm. We believe that the sinogram interpolation method can be applied in various reconstruction studies using the analytic reconstruction algorithm. Therefore, the sinogram interpolation method can improve the image quality in sparse-angular sampling and be applied to CT applications.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.632-641
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• 2004

In this paper, a new intermediate view reconstruction method employing a stereo image rectification algorithm by which an uncalibrated input stereo image can be transformed into the calibrated one is suggested and its performance is analyzed. In the proposed method, feature point are extracted from the stereo image pair though detection of the corners and similarities between each pixel of the stereo image. And then, using these detected feature points, the moving vectors between stereo image and the epipolar line is extracted. Finally, the input stereo image is rectified by matching the extracted epipolar line between the stereo image in the horizontal direction and intermediate views are reconstructed by using these rectified stereo images. From some experiments on synthesis of the intermediate views by using three kinds of stereo image; a CCETT's stereo image of 'Man' and two stereo images of 'Face' ＆ 'Car' captured by real camera, it is analyzed that PSNRs of the intermediate views reconstructed from the calibrated image by using the proposed rectification algorithm are improved by 2.5㏈ for 'Man', 4.26㏈ for 'Pace' and 3.85㏈ for 'Car' than !hose of the uncalibrated ones. This good experimental result suggests a possibility of practical application of the unposed stereo image rectification algorithm-based intermediate view reconstruction view to the uncalibrated stereo images.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.839-842
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• 2000

A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.847-850
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• 2000

This paper proposes an image compressing and template matching algorithm for face image using GHA (Generalized Hebbian Algorithm). GHA is a part of PCA (Principal Component Analysis), that has single-layer perceptrons and operates and self-organizing performance. We used this algorithm for feature extraction of face shape, and our simulations verify the high performance for the proposed method. The shape for face in the fact that the eigenvector of face image can be efficiently represented as a coefficient that can be acquired by a set of basis is to compress data of image. From the simulation results, the mean PSNR performance is 24.08[dB] at 0.047bpp, and reconstruction experiment shows that good reconstruction capacity for an image that not joins at leaning.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.714-718
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• 2009

3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.

• ETRI Journal
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• v.35 no.5
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• pp.931-934
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• 2013

The weight calculation in an iterative algorithm is the most computationally costly task in computed tomography image reconstruction. In this letter, a fast algorithm to speed up the weight calculation is proposed. The classic square pixel rotation approximate calculation method for computing the weights in the iterative algorithm is first analyzed and then improved by replacing the square pixel model with a circular pixel model and the square rotation approximation with a segmentation method of a circular area. Software simulation and hardware implementation results show that our proposed scheme can not only improve the definition of the reconstructed image but also accelerate the reconstruction.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1304-1310
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• 2021

Two-phase flow, especially gas-liquid two-phase flow, has a wide application in industrial field. The diagnosis of two-phase flow parameters, which directly determine the flow and heat transfer characteristics, plays an important role in providing the design reference and ensuring the security of online operation of two-phase flow system. Computer tomography (CT) is a good way to diagnose such parameters with imaging method. This paper has proposed a novel image reconstruction method for thermal neutron CT of two-phase flow with improved simulated annealing (ISA) algorithm, which makes full use of the prior information of two-phase flow and the advantage of stochastic searching algorithm. The reconstruction results demonstrate that its reconstruction accuracy is much higher than that of the reconstruction algorithm based on weighted total difference minimization with soft-threshold filtering (WTDM-STF). The proposed method can also be applied to other types of two-phase flow CT modalities (such as X(𝛄)-ray, capacitance, resistance and ultrasound).