• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.167-173
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• 2023

X-ray general radiography is the simplest and most important one to get a lot of information. Nevertheless, current x-ray general radiography does not observation in-depth observation. Information about the anatomy of the human body and changes in disease in x-ray general radiography can be obtained but it is difficult to determine the size and shape of the actual lesion due to the disadvantage of expanding the image. In this study, PA and LAT images were acquired and cancer magnification was calculated in the images by measuring the distance of cancer samples. By adjusting the magnification the actual cancer length and thickness were measured and compared with the CT image and the actual cancer sample size. After the PA and LAT images of the inserted 6.0 mm cancer sample were obtained and the magnification was corrected, the length was 5.9 mm and the thickness was 6.1 mm. This value was measured similarly to the actual. The problem of obtaining the magnification that needs to know the actual length from the detector to the cancer sample was secured by obtaining the magnification through PA and LAT images and it is possible to accurately measure the cancer sample size. X-ray general radiography may provide useful information in situations where CT imaging is difficult.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.

• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.3
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• pp.249-258
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• 2013

The purpose of this study was to investigate how image magnification in dental panoramic radiography is influenced by object position. Five metal balls (4 mm in diameter, 2 for the anterior and 3 for the posterior region on the right side) were placed above alveolar crest of dry skull considering extraction socket and dental arch. Dry skull was radiographed using OP-100D (Instrumentarium Imaging Co., Tuusula, Finland) at proper and displaced position along the sagittal and transverse plane at 3 mm, 6 mm, 9 mm, 12 mm and 15 mm using special mount which can control precise movement. Images were stored in DICOM files and were measured by ruler equipped within INFINITT PACS software (Infinitt Co., Ltd., Seoul, Korea). The mean horizontal magnification was 1.224-1.439 and mean vertical magnification was 1.286 - 1.345 at proper position. Vertical magnification resulted in less variation (1.245-1.418) than horizontal magnification (0.798-6.297) according to the sagittal and transverse displacements. Head positioning is important for linear measurement on panoramic radiography and inclusion of standard object (for instance, metal ball) is helpful to anticipate exact magnification of panoramic radiographs at various location.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.1-4
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• 2007

In this paper, we propose an efficient scheme for automatically detecting highlight scenes in soccer videos. Highlights are defined as shooting scenes and goal scenes. Through the analysis of soccer videos, we notice that most of highlight scenes are shown around the goal post area. It is also noticed that the TV camera zooms in a setter player or spectators after the highlight stones. Detection of highlight scenes for soccer videos consists of three steps. The first step is the extraction of the playing field using a statistical threshold. The second step is the detection of goal posts. In the final step, we detect a zooming of a soccer player or spectators by using connected component labeling of non-playing field. In order to evaluate the performance of our method, the precision and the recall are computed. Experimental results have shown the effectiveness of the proposed method, with 95.2% precision and 85.4% recall.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.703-712
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• 2019

Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.23-30
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• 2012

This paper proposes an improved steganalysis method to detect the existence of secret message. Firstly, we magnify the small stego noise by multiplying the speckle noise to a given image and then we estimate the denoised image by using the soft thresholding method. Because the noises are not perfectly eliminated, some noises exist in the estimated cover image. If the given image is the cover image, then the remained noise will be very small, but if it is the stego image, the remained noise will be relatively large. The parent-child relationship in the wavelet domain will be slighty broken in the stego image. From this characteristic, we extract the joint statistical moments from the difference image between the given image and the denoised image. Additionally, four statistical moments are extracted from the denoised image for the proposed steganalysis method. All extracted features are used as the input of MLP(multilayer perceptron) classifier. Experimental results show that the proposed scheme outperforms previous methods in terms of detection rates and accuracy.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.185-188
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• 2002

With improved network system and development of computer technology, a lot of hospitals are equipping PACS that deals with process and transmission of the medical images. Owing to equipment of PACS the problems on transmission and storage of the medical images were treated. The way to solve the problems is to use various image processing techniques and compression methods This paper describes RLC in lossless image compression method, JPEG using DCT in loss image compression applied to medical images as way implementing DICOM standard. Now the medical images were compressed with Wavelet transform method have been taken advantage of image process. And compression rate of each compression methods was analyzed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.94-101
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• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.511-513
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• 2000

PaP Smear 테스트는 자궁 경부암 진단에 가장 효율적인 방법으로 알려져 있다. 그러나 이 방법은 높은 위 음성률(false negative error, 15~50%)을 나타내고 있다. 이런 큰 오류율은 주로 다량의 세포 검사에 기인하여, 자동화 시스템의 개발이 절실히 요구되고 있다. 본 논문은 자궁 경부암의 특징인 군집을 이루는 암세포를 인식할 수 있는 시스템을 제안한다. 시스템은 두 부분으로 나누어진다. 첫 단계에서는 저 배율(100배)에서 간단한 영상처리와 최소 근접 트리(Minimum Spanning Tree)를 통해 군집을 이루는 세포를 찾는다. 두 번째 단계서는 고 배율(400배)로 확대하여 군집 세포들로부터 여러 가지 특징을 추출한 후 KNN(k-Neighbor) 방법을 통해 인식하는 단계이다. 50개의 영상 (640X 480, RGB True Color 25 개의 100배 영상 , 25개의 400배 영상)이 실험에 사용되었다. 한 영상을 처리하는데 약 3초 (2.984초) 소요되었으며, 이는 region growing(20초)나 split and merge(58초) 방법 보다 덜 소요되었다. 100배 영상에서 정상과 비정상의 두 그룹으로 나누었을 경우에는 96%의 높은 인식율을 나타내었으나 비정상을 다시 5개의 그룹으로 나누었을 때는 45%로 나타내었다. 이는 영역 추출(segmentation) 단계에서 오류와 트레이닝 데이터의 비정확성에 기인한다. 400배 영상에서는 각각 92%와 30%로 나타내었다. 이는 영역추출 단계에서 사용한 Watershed 방법의 오류로 기인한 것으로 본다.