• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.107-114
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• 2014

Purpose : The purpose of this study is evaluation for the applicability of O-MAR(Metal artifact Reduction for Orthopedic Implants)(ver. 3.6.0, Philips, Netherlands) in head & neck radiation treatment planning CT with metal artifact created by dental implant. Materials and Methods : All of the in this study's CT images were scanned by Brilliance Big Bore CT(Philips, Netherlands) at 120kVp, 2mm sliced and Metal artifact reduced by O-MAR. To compare the original and reconstructed CT images worked on RTPS(Eclipse ver 10.0.42, Varian, USA). In order to test the basic performance of the O-MAR, The phantom was made to create metal artifact by dental implant and other phantoms used for without artifact images. To measure a difference of HU in with artifact images and without artifact images, homogeneous phantom and inhomogeneous phantoms were used with cerrobend rods. Each of images were compared a difference of HU in ROIs. And also, 1 case of patient's original CT image applied O-MAR and density corrected CT were evaluated for dose distributions with SNC Patient(Sun Nuclear Co., USA). Results : In cases of head&neck phantom, the difference of dose distibution is appeared 99.8% gamma passing rate(criteria 2 mm / 2%) between original and CT images applied O-MAR. And 98.5% appeared in patient case, among original CT, O-MAR and density corrected CT. The difference of total dose distribution is less than 2% that appeared both phantom and patient case study. Though the dose deviations are little, there are still matters to discuss that the dose deviations are concentrated so locally. In this study, The quality of all images applied O-MAR was improved. Unexpectedly, Increase of max. HU was founded in air cavity of the O-MAR images compare to cavity of the original images and wrong corrections were appeared, too. Conclusion : The result of study assuming restrained case of O-MAR adapted to near skin and low density area, it appeared image distortion and artifact correction simultaneously. In O-MAR CT, air cavity area even turned tissue HU by wrong correction was founded, too. Consequentially, It seems O-MAR algorithm is not perfect to distinguish air cavity and photon starvation artifact. Nevertheless, the differences of HU and dose distribution are not a huge that is not suitable for clinical use. And there are more advantages in clinic for improved quality of CT images and DRRs, precision of contouring OARs or tumors and correcting artifact area. So original and O-MAR CT must be used together in clinic for more accurate treatment plan.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.409-415
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• 2016

Recently several techniques for disaster prevention based on IoT(Internet of Things) are being developed. In this paper, a new smart pattern classification method for electric loads is proposed. CT(Current Transformer) data are extracted from electric loads, and then the sampled CT data are converted using FFT and MFCC. FFT and FMCC data are used for the input data of neural networks. Experiments were conducted using FFT and MFCC data for 7 kinds of electric loads. Experiments results indicate the superiority of MFCC in comparison to FFT.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.322-325
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• 2011

스테레오 비전 시스템에서 전통적인 매칭 알고리즘으로 SAD(Sum of Absolute Differences), SSD(Sum of Squared Differences), NCC(Normalized Cross Correlation) 등 다양한 알고리즘이 존재한다. 그러나 하드웨어로 실시간 처리를 위한 시스템을 구현하기 위해서는 리소스가 한정 되어있다는 제약 때문에 많은 연구에서 SAD 혹은 RT(Rank Transform), CT(Census Transform)를 많이 사용하게 된다. FPGA 내부에는 BRAM(Block RAM)과 MAC(multiply-accumulator)인 DSP슬라이스가 이미 존재한다. 본 논문에서는 BRAM과 DSP로직을 활용해서 전통적인 매칭 알고리즘 중에서 연산기 사용이 가장 많은 NCC를 FPGA로 실시간 처리 가능한 하드웨어 구조를 제안한다.

• Conservation Science in Museum
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• v.22
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• pp.15-26
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• 2019

This study was focused on the three-dimensional convergence modeling that can multilaterally analyze internal and external shapes of the Sam-Chongtong Hand Canon by optical precision scanning optimized for acquiring the surface shape and X-ray CT scanning used for obtaining the internal shape. First, the scanning results were converted by compatible extension, after which three-dimensional deviation analysis was conducted to verify mutual conformities. Accordingly, most (56.98%) deviations between the two scanning models was found be ±0.1mm. This result did not influence registration and merging based on the ICP algorithm. The merged data exhibited the external surface color, detailed shapes, internal width, and structure of the hand canon. The three-dimensional model based on optical surface scanning and X-ray CT scanning can be used for traditional technique interpretation as well as digital documentation of cultural heritage. In the future, it will contribute to deliver accessible scientific information of exhibits for visitors.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.1-6
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• 2022

Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.

• 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 Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.212-218
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• 2020

There are dozens of different types of lesions that can be diagnosed through chest X-ray images, including Atelectasis, Cardiomegaly, Mass, Pneumothorax, and Effusion. Computed tomography(CT) test is generally necessary to determine the exact diagnosis and location and size of thoracic lesions, however computed tomography has disadvantages such as expensive cost and a lot of radiation exposure. Therefore, in this paper, we propose a deep learning algorithm for judging the presence or absence of lesions in chest X-ray images as the primary screening tool for the diagnosis of thoracic lesions. The proposed algorithm was designed by comparing various configuration methods to optimize the judgment of presence of lesions from chest X-ray. As a result, the evaluation rate of lesion presence of the proposed algorithm is about 1% better than the existing algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1637-1643
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• 2022

In recent years, the demand for image-processing technology in digital marketing has increased due to the expansion and diversification of the digital market, such as video, security, and machine intelligence. Noise-processing is essential for image-correction and reconstruction, especially in the case of sensitive noises, such as in CT, MRI, X-ray, and scanners. The two main salt and pepper noises have been actively studied, but the details and edges are still unsatisfactory and tend to blur when there is a lot of noise. Therefore, this paper proposes an algorithm that applies a weight-based Euclidean distance equation to the partial mask and uses only the non-noisy pixels that are the most similar to the original as effective pixels. The proposed algorithm determines the type of filter based on the state of the internal pixels of the designed partial mask and the degree of mask deterioration, which results in superior noise cancellation even in highly damaged environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.47-53
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• 2023

Devices such as X-Ray, CT, MRI, scanners, etc. can generate S&P noise from several sources during the image acquisition process. Since S&P noise appearing in the image degrades the image quality, it is essential to use noise reduction technology in the image processing process. Various methods have already been proposed in research on S&P noise removal, but all of them have a problem of generating residual noise in an environment with high noise density. Therefore, this paper proposes a filtering algorithm based on a three-dimensional plane equation by setting the grayscale value of the image as a new axis. The proposed algorithm subdivides the local mask to design the three closest non-noisy pixels as effective pixels, and applies cosine similarity to a region with a plurality of pixels. In addition, even when the input pixel cannot form a plane, it is classified as an exception pixel to achieve excellent restoration without residual noise.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.223-229
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• 2001

In this paper, we have compared the direct fan-beam reconstruction method with the rebinning method in terms of computation time and spatial resolution using computer simulation. As a result, the direct fan-beam method is superior to the rebinning method in the spatial resolution though the former needs longer computation time. However, if we adopt the quarter-detector-offset technique to improve the spatial resolution, the rebinning method outperforms the direct fan-beam method. The computation times have been evaluated using the fast algorithms optimized to reduce the number of interpolation calculations at the back-projection, and the spatial resolutions have been compared using the computer generated phantoms.