• Journal of radiological science and technology
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• v.40 no.2
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• pp.197-203
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• 2017

The purpose of this study was to measure signal to noise ratio (SNR) according to change of equalization from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 87 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p < 0.05). In SNR results, with the quality of distributions in the order of original chest image, original chest image heart shadow and equalization chest image, equalization chest image heart shadow(p < 0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the histogram equalization chest image.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.131-136
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• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1059-1062
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• 1999

Recently, X-ray chest radiograph is showing a tendency to take an image of digital radiograph so as to diagnose the pathological pattern of chest in a usual. When the radiologist observes the chest image derived from digital radiograph system on the monitor. he feels difficult to find out because of the sensitivity of chest radiograph. It takes amount of time to adjust the proper image for diagnosis. Therefore, we provided the result and the method of the optimal image equalization for image enhancement.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.264-269
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• 2015

The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.45-51
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• 2022

This study compared DLP values along with phantom entrance surface doses and the image quality of chest CT scans made using a Care Dose 4D+Care kV System, scans that are made using only the Care Dose 4D function, and scans that are made with changes made by applying 80 kVp, 100 kVp, 120 kVp, and 140 kVp to the Care Dose 4D and tube voltage to search for methods to maintain the highest image quality with minimal patient doses. It was shown that DLP values decreased 6.727% when scans were taken with Chest Care Dose 4D + Care kV semi 100 and 6.481% when scans were taken with Chest Care Dose 4D + Care kV. With Chest Non as a standard, skin surface doses decreased 16.519% when scans were taken with Chest Care Dose 4D + Care kV semi 100 and 15.705% when scans were taken with Chest Care Dose 4D + Care kV. With comparisons of image quality, when comparisons were made with Chest Non, comparisons made of SNR values and CNR values in all scanning conditions including Care Dose 4D + Care kV showed that there were no significant differences at P>0.05. Imaging using Chest Care Dose 4D + Care kV in chest CT showed that exposure doses decreased similarly to result values gained from the best conditions through manual adjustments of kV and mAS, and there were no significant differences in image SNR and CNR. If the Chest Care Dose 4D + Care kV function is used, image quality is maintained and patient exposure to radiation can be reduced.

• Journal of the Korean Society of Radiology
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• v.11 no.6
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• pp.443-451
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• 2017

The purpose of this study was to comparison of measure signal to noise ratio (SNR) according to change of kernel size from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 100 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p<0.05). In SNR results, with the quality of distributions in the order of kernel size 9*9 image, kernel size 7*7 image and original chest image, kernel size 3*3 image (p<0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the kernel size chest image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.1
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• pp.49-55
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• 2000

Recently, X-ray chest rediography is showing a tendency to take an image of digital radiography so as to diagnose the pathology of chest in a usual. When the radiologist observes the chest image derived from digital radiography system on the monitor, he feels difficult to find out the pathological pattern because the quality of chest radiography is unequal. It takes amount of time to adjust the proper image for diagnosis. Therefore, we propose the method of the chest image equalization using neural networks and provide the compared result with histogram equalization method.

• Progress in Medical Physics
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• v.20 no.4
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• pp.199-207
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• 2009

It is important to initialize Image Plate (IP) completely for removing residual latent image by sodium lamp for reliability and repeatability of computed radiography (CR) system. The purpose of this study was to evaluate latent images of computed radiography (CR) images respect to delay time after erasure of foregone latent image and its effect, and erasure level. Erasure thoroughness for CR acceptance test from American Association of Physicist in Medicine (AAPM) Report 93 (2006) was also evaluated. Measurements were made on a CR (Agfa CR 25; Agfa, BELGIUM) system. Chest postero-anterior (PA), Hand PA, L-spine lateral radiographs were chosen for evaluation. Chest phantom (3D-torso; CIRS, USA) was used for Chest PA and L-spine lateral radiography. For Hand PA radiography, projections was done without phantom. Except Hand PA radiographs, noise was increased with delay time, and ghost image was appeared on overexposed area. Effect of delay after erasure on latent image was not seen on naked eye, but standard deviation (SD) of pixel value on overexposed area was relatively higher than that of other areas. On Hand PA and Chest PA radiographs, noise were not occurred by adjustment of erasure level. On L-spine lateral images at lower erasure level than standard level, noise including ghost image were occurred because of high tube current. Erasure thoroughness of CR system in our department was to be proved by these evaluation. The results of this study could be used as a baseline for IP initialization and reliability of CR images.