• Korean Journal of Digital Imaging in Medicine
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• v.16 no.2
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• pp.25-29
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• 2014

The full-field digital mammography (FFDM), which has been known as a digital breast imaging system, carries out more outstanding performance than the screen-film mammography in overall image quality, skin & nipple, description of pectoral muscle and expression of micro-calcification. Thus, in this thesis, I perform experiments for both the enhancement of image quality and accurate estimation of the result in question, when detecting the very tiny-sized lesions in mammography. The image of digital breast X-rays is the important diagnostic tool for detecting early breast cancer and micro calcification lesion. The experiment of how much compression rate has an effect on the result of diagnosis in the case of microcalcification lesion, with JPEG2000 40:1 compression and over 50% enlargement led to obscure or definitely unacceptable diagnostic results is performed. And in another study of assessment of PSNR degree. I recognized the importance of standardized management system in mammography, where not to mention the accurate reading of the image has the most crucial role in diagnosis

• Journal of the Korean Society of Radiology
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• v.8 no.4
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• pp.147-153
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• 2014

We tried to assessment about characteristics of image through quantitative evaluation method and qualitative evaluation method in Full Field Digital Mammography. It satisfied an approval standard of ten score regardless of compression ratio measuring detection score after compressing and appling an algorithm of JPEG2000 orJPEG compression targeting ACR accreditation phantom. Also, it was apparent that when we selected and compressed the image of real fine lesion and measured a change of diagnosis ability magnifing over 50 percent after compressing over 20:1 ratio, it had a strong influence on diagnosis ability. We realized that the difference between the original image according to compression ratio measuring a quantitative evaluation which is PSNR,RMSE,MAE and SSIM was relatively allowable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4578-4589
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• 2011

The technologies of image acquisition, display, and storage of the breast have been developed in their specialized fields in recent years. The image acquisition system is a device that absorbs and stores images after examining breast tissue using X-ray. Due to the specificity and sensitivity of imaging, the early detection of breast cancer is feasible. In this paper, the current technologies for digital mammography are reviewed, and we propose a digital mammography and evaluate the performance of the implemented system.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.29-34
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• 2019

In this study, we measure dose against various density and thickness using phantom to compare FFDM to DBT of Digital mammography equipment and evaluate usefulness of DBT through compare the image quality of FFDM and DBT. We use mammography equipment, Selenia Dimensions ; this is able to examine breast by both FFDM and DBT, The results are that when the thickness of phantom is 6cm or more and density is 70% or more and the thickness of phantom is 7cm or more and density is 50% or more, AGD of DBT is lower than that of FFDM. The evaluation results of image quality are that in the tumor and small calcification group that composed by mammary tissue and fat, FFDM is great and in fibrin, DBT is great. But in the all thicknesses of BR3D phantom that reflected overlapped tissue of breasts, DBT is great in calcification group, fibrin and tumor. DBT is greater image quality and lower dose more than FFDM in Thick and high density breast, Therefore, DBT is more useful in Korean women's breast that is characterized dense breast than FFDM.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.9
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• pp.4109-4114
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• 2015

Background: Full-field digital mammography (FFDM) with dense breasts has a high rate of missed diagnosis, and digital breast tomosynthesis (DBT) could reduce organization overlapping and provide more reliable images for BI-RADS classification. This study aims to explore application of COMBO (FFDM+DBT) for effect and significance of BI-RADS classification of breast cancer. Materials and Methods: In this study, we selected 832 patients who had been treated from May 2013 to November 2013. Classify FFDM and COMBO examination according to BI-RADS separately and compare the differences for glands in the image of the same patient in judgment, mass characteristics display and indirect signs. Employ Paired Wilcoxon rank sum test was used in 79 breast cancer patients to find differences between two examine methods. Results: The results indicated that COMBO pattern is able to observe more details in distribution of glands when estimating content. Paired Wilcoxon rank sum test showed that overall classification level of COMBO is higher significantly compared to FFDM to BI-RADS diagnosis and classification of breast (P<0.05). The area under FFDM ROC curve is 0.805, while that is 0.941 in COMBO pattern. COMBO shows relation of mass with the surrounding tissues, the calcification in the mass, and multiple foci clearly in breast cancer tissues. The optimal sensitivity of cut-off value in COMBO pattern is 82.9%, which is higher than that in FFDM (60%). They share the same specificity which is both 93.2%. Conclusions: Digital Breast Tomosynthesis (DBT) could be used for the BI-RADS classification in breast cancer in clinical.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.162-168
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• 2007

For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.

• Journal of the Korean Society of Radiology
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• v.10 no.3
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• pp.215-222
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• 2016

X-ray mammography is the most effective method for the diagnosis of calcified lesions of various breast diseases. To reduce patient dose and to obtain optimal image required for diagnosis, the performance of the mammography system should be maintained continuously. Because the target (anode) angle of the X-ray tube is measured from the central X-ray, the effective angle can be slightly different in view of the position on the detector, which can result in degrading spatial resolution of the imaging within the field of view. In this study, we measured the MTF to examine spatial resolution for positions on the detector in the digital mammography system. For a tungsten wire of $50{\mu}m$ diameter, the highest spatial frequency was obtained. It meant that a wire diameter for measuring MTF through LSF should be small compared to the pixel size of the detector used in the mammography system. The spatial resolution showed slightly different performance according to positions on the detector. The center position gave the best spatial resolution and positions away from the center showed the degraded performance although the difference of the spatial resolution was small. The effective focal spot size of the full width at half maximum also showed similar result. It concluded that the slightly increase of the effective focal spot size gave the degradation of the spatial resolution for positions on the detector.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.