• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1684-1688
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• 2005

In this paper, authentication technologies for X-ray inspection images in container merchandises are introduced and a method of authentication for X-ray inspection images is proposed. Until now, X-ray images of container merchandises have been managed without any authentication of inspection results and environments, it means that there was no any action for protection of illegal copy and counterfeiting of X-ray images from inspection results. Here, authentication identifies that who did inspect container X-ray images and, whether the container X-ray images were counterfeited or not. Our proposed algorithm indicates to put important information about X-ray inspection results on an X-ray image without affecting quality of the original image. Therefore, this paper will be useful in determining an appropriate technology and system specification for authentication of X-ray inspection images. As a result of experiment, we find that the information can be embedded to X-ray image without large degradation of image quality. Our proposed algorithm has high detection ratio by Quality 20 of JPEG attack.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.319-331
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• 2022

The capability to examine an X-ray image is so far a challenging task. In this work, we suggest a practical and novel algorithm based on image fusion to inspect the issues such as background noise, blurriness, or sharpness, which curbs the quality of dual-energy X-ray images. The current technology exercised for the examination of bags and baggage is "X-ray"; however, the results of the incumbent technology used show blurred and low contrast level images. This paper aims to improve the quality of X-ray images for a clearer vision of illegitimate or volatile substances. A dataset of 40 images was taken for the experiment, but for clarity, the results of only 13 images have been shown. The results were evaluated using MSE and PSNR metrics, where the average PSNR value of the proposed system compared to single X-ray images was increased by 19.3%, and the MSE value decreased by 17.3%. The results show that the proposed framework will help discern threats and the entire scanning process.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.572-576
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• 1997

X-ray laminography and DT(digital tomosynthesis) that can form a cross-sectional image of 3-D objects promise to be good solutions for inspecting interior defects of industrial products. The major factors of the digital tomosynthesis that influence on the quality of x-ray cross-sectional images are also discussed. The quality of images acquired from the DT system varies according to image synthesizing methods, the number of images used in image synthesizing, and X-ray projection angles. In this paper, a new image synthesizing method named 'log-root method' is proposed to get clear and accurate cross-sectional images, which can reduce both artifact and blurring generated by materials out of focal plane. To evaluate the quality of cross-sectional images, two evaluating criteria: (1) shape accuracy and (2) clearness in the cross-sectional image are defined. Based on this criteria, a series of simulations were performed, and the results show the superiority of the new synthesizing method over the existing ones such as averaging and minimum method.

• 한국가시화정보학회:학술대회논문집
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• 2002.11a
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• pp.31-34
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• 2002

Imaging techniques using x-ray beam at high energies (>6KeV) such as contact radiography, projection microscopy, and tomography have been used to nondestructively discern internal structure of objects in material science, biology, and medicine. This paper introduces the x-ray micro-imaging method using 1B2 micro-probe line of PAL (Pohang Accelerator Laboratory). Cross-sectional information on low electron density materials can be obtained by probing a sample with coherent synchrotron x-ray beam in an in-line holography setup. Living organism such as plants, insects are practically transparent to high energy x-rays and create phase shift images of x-ray wave front. X-ray micro-images of micro-bubbles of $20\~120\;{\mu}m$ diameter in an opaque tube were recorded. Clear phase contrast images were obtained at Interfaces between bubbles and surrounding liquid due to different decrements of refractive index.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.886-890
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• 2013

In this paper, a 3D coordinates acquisition method for a mechanical assembly is developed by using multiview X-ray images. The multi-view X-ray images of an object are obtained by a rotary table. From the rotation transformation, it is possible to obtain the 3D coordinates of corresponding edge points on multi-view X-ray images by triangulation. The edge detection algorithm in this paper is based on the attenuation characteristic of the X-ray. The 3D coordinates of the object points are represented on a graphic display, which is used for the inspection of a mechanical assembly.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.11-19
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• 2023

To remove unnecessary background from tooth X-ray images and enhance the sharpness of tooth and gum images, image processing techniques including contrast adjustment and histogram equalization are used. The introduction of two methods for detecting the boundary of the tooth and gum region and separating the tooth and gum from the background. In both cases, the background of the tooth X-ray images could be removed as a result, improving the quality of the images. The proposed method improves MTF (Modulation Transfer Function), an image performance indicator, as a result of measuring MTF. The original image's spatial frequency ranged from 4.73 to 11.40 lp/mm at the 10% response, whereas the proposed image's spatial frequency ranged from 10.90 to 11.85 lp/mm, giving uniformly enhanced results. In contrast, tooth and gums could not be completely separated from the background using Apple's Lift subject from background function.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.108-117
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• 2015

If typical contrast enhancement algorithms for natural images are applied to X-ray images, they may cause artifacts such as overshooting or produce unnatural visual quality because they do not consider inherent characteristics of X-ray images. In order to overcome such problems, we propose a locally adaptive block-based contrast enhancement algorithm for X-ray images. After we derive a weighted cumulative distribution function for each block, we apply it to each block for contrast enhancement. Then, we obtain images that are removed from block effect by adopting block-based overlapping. In post-processing, we obtain the final image by emphasizing high frequency components. Experimental results show that the proposed block-based contrast enhancement algorithm provides at maximum 5-times higher visual quality than the exiting algorithm in terms of quantitative contrast metric.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.413-413
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• 2000

In this paper, we propose a series of calibrations f3r the x-ray three dimensional imaging system. In the developed x-ray system, a three dimensional inner and outer shape of an object can be reconstructed out of two dimensional transmitted x-ray image set, which are acquired by projecting x-ray to the object from different views. To achieve this, a reconstruction algorithm which estimates and updates the three dimensional volume from x-ray images is developed. The algorithm is named as uniform and simultaneous algebraic reconstruction technique(USART) which is an iterative method estimating a 3D volume based on its projected images. In this method, it is assumed that the imaging conditions that are the relative positions between the x-ray sources, object and the image planes are blown. Practically it is not easy to know the three dimensional coordinate of the components of the system, since the x-ray is not visible and the image distortions are present due to the optical components in the system. In this paper, methods of correcting image distortions are present firstly. Then the coordinates of the x-ray systems are calibrated from the x-ray images of the grid pattern. Some experimental results on these calibrations are present and discussed.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.97-107
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• 2015

The initial X-ray images obtained from a digital X-ray machine have a wide data range and uneven brightness level than normal images. In particular, in Chest X-ray images, it is necessary to improve naturally all of the parts such as ribs, spine, tissue, etc. These X-ray images can not be improved enough from conventional image quality enhancement algorithms because their characteristics are different from ordinary images'. This paper proposes to eliminate unnecessary background from an input image and expand the histogram range of the image. Then, we adjust the weight per frequency band of the image for improvement of contrast and sharpness. Finally, jointly taking the advantages of global contrast enhancement and local contrast enhancement methods we obtain an improved X-ray image suitable for effective diagnosis in comparison with the existing methods. Experimental results show quantitatively that the proposed algorithm provides better X-ray images in terms of the discrete entropy and saturation than the previous works.