• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송공학회 2015년도 하계학술대회
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• pp.538-539
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• 2015

일반 영상의 화질을 개선하기 위해 다양한 알고리즘이 존재한다. 하지만 X-ray 영상의 경우 일반 영상과 특성이 다르기 때문에 기존의 화질 개선 알고리즘으로는 진단에 적합한 화질을 얻을 수 없다. 디지털 X-ray 기기로부터 처음 획득된 X-ray 영상은 데이터 범위가 일반 영상에 비해 넓고 밝기 레벨이 고르지 못하다. 특히 Chest X-ray 영상의 경우 다양한 이유로 촬영하기 때문에 갈비뼈와 혈관, 척추 뼈 등 특성이 다른 모든 부위들을 자연스럽게 개선할 필요가 있다. 본 논문은 영상의 불필요한 배경 성분을 제거하여 특정 밝기에 밀집되어 있는 데이터들의 히스토그램 범위를 확장시키고 주파수 대역 별 가중치를 조절하여 대비 및 선명도를 향상시킨다. 마지막으로 전역적 대비 개선 기법과 지역적 대비 개선 기법의 장점을 취하여 진단에 적합하도록 개선된 Chest X-ray 영상을 얻는다.

• Progress in Medical Physics
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• 제19권3호
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• pp.150-156
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• 2008

An applied technique of contrast enhancement for X-ray image is proposed which is based on combined enhancement of scaling and wavelet coefficients in discrete wavelet transform space. Conventional contrast enhancement methods such as contrast limited adaptive histogram equalization (CLAHE), multi-scale image contrast amplification (MUSICA) and gamma correction were applied on scaling coefficients to enhance the contrast of an original. In order to enhance the detail as well as reduce the blurring caused by up scaling of contrast modified scale coefficients from lower resolution, the sigmoid manipulation function was used to manipulate wavelet coefficients. The contrast detail mammography (CDMAM) phantom was imaged and processed to measure the image line profile of results and contrast to noise ratio (CNR) comparatively. The proposed technique produced better results than direct application of various contrast enhancement methods on image itself. The proposed method can enhance contrast, and also suppress the amplification of noise components in a single process. It could be useful for various applications in medical, industrial and graphical images where contrast and detail are of importance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• 제44권6호
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• pp.42-48
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• 2007

In x-ray imaging system, two kinds of noises are involved. First, the charge generated from the radiation interaction with the detector during exposure. Second, the signal is then added by readout electronics noise. But, x-ray images are not modeled by Gaussian noise but as the realization of a Poisson process. In this paper, we apply a new approach to remove Poisson noise from medical X-ray image in the wavelet domain, the applied methods shows more excellent results in cone-beam CT.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제37권7C호
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• pp.601-609
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• 2012

In this paper, a limited-view CT image reconstruction method was studied to reduce the scan times and the X-ray dose for the patients. To reduce streak artifacts which is caused by insufficient number of views, we introduce a sinogram interpolation method based on image matching. Image matching is achieved using the characteristics of the neighboring views including intensity, gradient and distance between the pixels. Interpolation is performed using the image matching results.. A numerical phantom and Al-acryl phantom were used for evaluating the effectiveness of the proposed interpolation method. The results showed that streak artifacts were reduced in the reconstructed images while the details of the images were preserved. Moreover, maximum 5% improvements in terms of PSNR were observed.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제32권4C호
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• pp.389-395
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• 2007

The Film X-ray and the CCD method of current medical image system have the disadvantages such as required large place and diagnosis time. In this paper, we implement an effective medical image system using TXT-DXD method's digital X-ray detector(DR1000C). The implemented medical image system has advantages of placing efficiency and short diagnosis time. In order to make the image out of the system more effective, we develop an LCD(Liquid Crystal Display) control driver, having the resolution of 1900*1200. And we propose an enhancement unsharp masking method to update image enhancement of DR1000C medical image system, and compare it with the current methods.

• Journal of the Korean Society of Radiology
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• 제10권6호
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• pp.451-456
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• 2016

Radiation dose to patient is unavoidable when diagnosis is carried out using X-ray. Radiation diagnosis using dual energy X-ray was examined to verify the possibility of medical applications by SNR and image scoring. The dual energy X-ray was realized by combining together two image plates and filter of 0.5 mm thick Cu or Al. Under one X-ray exposure, contrast enhanced image was obtained using two images of image plates. The enhanced image showed higher SNR and image score compared to the first image which was the image recorded with the first image plate. The dual energy X-ray technique would be a very useful method for obtaining higher SNR image and for realizing very low dose, and could be applied to medical applications.

• Journal of the Korean Society for Nondestructive Testing
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• 제27권1호
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• pp.8-14
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• 2007

X-ray digital tomosynthesis is widely used in the nondestructive testing and evaluation, especially for the printed circuit boards (PCBs). In this study, we propose a simple method to reduce the blur artefact, frequently claimed in the conventional digital tomosynthesis based on SAA (shift-and-add) algorithm, and thus restore the image sharpness. The proposed method is basically based on the SAA, but has a correction procedure by finding blur artefacts from the forward-and back-projection for the firstly obtained, manipulated backprojection data. The manipulation is the replacement of the original data at the POI (plane-of-interest) by zeros. This method has been compared with the conventional SAA algorithm using the experimental measurements and Monte Carlo simulation for the designed PCB phantom. The comparison showed a much enhancement of sharpness in the images obtained from the proposed method.

• Journal of the Korean Society of Radiology
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• 제15권2호
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• Journal of Internet Computing and Services
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• 제11권1호
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• pp.21-26
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• 2010

In this paper, film uniformity is improved by the bolt-mapping of convolutional image processing between bolts and a film using soft X-ray based in-line LCD TV film thickness controls. The automatic film thickness analysis of 3 inter-bolt's convolution function is proposed for the reduction of offset error from the X-ray changing of line scan in film profiles.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.579-580
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• 2023

본 논문에서는 동물의 십자인대 질환의 진단지표인 경골고원각도(TPA)를 자동으로 측정하는 딥러닝 소프트웨어 기법을 제안한다. 동물 X-ray 영상에서 나타나는 피사체의 위치와 형태에 대한 다양한 변이는 TPA(Tibial Plateau Angle) 지표 산출에 필요한 특징점 검출과정에서 학습 효율을 현저하게 저하시킨다. 이에 본 연구에서는 YOLO(You Only Look Once) 기반 모델을 사용하여 일차적으로 경골영역의 분할 단계를 수행하고, 이어서 경골 상단부의 과간융기와 복사뼈의 중심점을 찾는 과정을 Resnet 기반의 특징점 추출 모듈로서 구현함으로써 학습의 효율과 지표 검출의 정확도를 향상시켰다. 총 201 개의 실제 X-ray 영상을 사용하여 학습 속도와 영역 분할 및 특징점 추출의 정확도 측면을 고려함으로 제안된 이론의 타당성을 실험적으로 평가하였다.