• Journal of Biomedical Engineering Research
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• v.11 no.2
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• pp.209-216
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• 1990

Pulmonary nodules in chest X-Ray images were detected using the symmetric property of human lung and its performance was evaluated. Thls algorithm reduced the effect of background components and enhanced the nodule signals relatively. The image was divided and processed separately, the half with matched filter only, and the other half with warping and matched filter. This algorithm increased the entire detection rate by reducing False-Positive error and improving True-Positive detectability. Result shows 10-25 % improvement in detection rate compared with the conventional alsorithm for nodules size of 10mm.

• Journal of Biomedical Engineering Research
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• v.10 no.1
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• pp.1-10
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• 1989

An automatic lung nodule detection algorithm was applied for digital radiographic images using Bit Slice Processor. In this algorithm, signal enhancing filtering and signal suppressing filtering were performed on the given digital chest image, respectively. Then we grit the dirt- frrence image from these filtered images, and hi-level island images were obtained by applying various threshold values. From the island images, we decided the suspicious nodules using size and circularity test, and marked them to alert radiologists. The performance of the atgorithm was analyzed with respect to the size, contrast and position of digitally synthesized nodules. This method presented 45.8% of true positive ratio for the nodules of lOw in diameter with 12-16 pixel value differnces.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.1
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• pp.90-97
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• 2018

Purpose The lung segment ratio which is obtained through quantitative analyses of lung perfusion scan images is calculated to evaluate the lung function pre and post surgery. In this Study, the planar image production methods by using Q-Metrix (GE Healthcare, USA) program capable of not only quantitative analysis but also computation of the segment ratio after having performed SPECT/CT are comparatively evaluated. Materials and Methods Lung perfusion scan and SPECT/CT were performed on 50 lung cancer patients prior to surgery who visited our hospital from May 1, 2015 to September 13, 2016 by using Discovery 670(GE Healthcare, USA) equipment. AP(Anterior Posterior)method that uses planar image divided the frontal and rear images into three rectangular portions by means of ROI tool while PO(Posterior Oblique)method computed the segment ratio by dividing the right lobe into three parts and the left lobe into two parts on the oblique image. Segment ratio was computed by setting the ROI and VOI in the CT image by using Q-Metrix program and statistically analysis was performed with SPSS Ver. 23. Results Regarding the correlation concordance rate of Q-Metrix and AP methods, RUL(Right upper lobe), RML(Right middle lobe) and RLL(Right lower lobe) were 0.224, 0.035 and 0.447. LUL(Left upper lobe) and LLL(Left lower lobe) were found to be 0.643 and 0.456, respectively. In the PO method, the right lobe were 0.663, 0.623 and 0.702, respectively, while the left lobe were 0.754 and 0.823. When comparison was made by using the Paired sample T-test, Right lobe were $11.6{\pm}4.5$, $26.9{\pm}6.2$ and $17.8{\pm}4.2$, respectively in the AP method. Left lobe were $28.4{\pm}4.8$ and $15.4{\pm}5.6$. The right lobe of PO had values of $17.4{\pm}5.0$, $10.5{\pm}3.6$ and $27.3{\pm}6.0$, while the left lobe had values of $21.6{\pm}4.8$ and $23.1{\pm}6.6$, thereby having statistically significant difference in comparison to the Q-Metrix method for each of the lobes (P<0.05). However, there was no statistically significant difference in Right middle lobe (P>0.05). Conclusion The AP method showed low concordance rate in correlation with the Q-Metrix method. However, PO method displayed high concordance rate overall. although AP method had significant differences in all lobes, there was no significant difference in Right middle lobe of PO method. Therefore, at the time of production of lung perfusion scan results, utilization of Q-Metrix method of SPECT/CT would be useful in computation of accurate resultant values. Moreover, it is deemed possible to expect obtain more practical sectional computation result values by using PO method at the time of planar image acquisition.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.625-632
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• 2020

In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.397-399
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• 2021

In this study, a basic study on the development of a multi-stimulation system was conducted to suppress lung cancer cell proliferation. Stimulation was applied to lung cancer cells using a photo-stimulating system and ultrasonic waves that generate a specific frequency, and the effect of inhibiting proliferation of cells was imaged and quantitatively evaluated. As a result of the experiment, when a single LED, single ultrasound stimulus were applied and ultrasound and LED stimuli were applied at the same time, meaningful results were shown in the proliferation rate of lung cancer cells.

• Journal of radiological science and technology
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• v.25 no.2
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• pp.39-45
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• 2002

Fifty patients with lung mass were studied to evaluate the effect of reconstruction algorithm on the CT number of lung mass and normal thoracic tissues. In each examination, the CT image of the lung mass was reconstructed using soft, standard, detail and bone algorithm. The results were shown as follows 1. the average maximum difference of lung mass density on the ROIs using 4 different algorithms was less than 1HU. 2. The maximum difference in the degree of lung mass enhancement was respectively $0.1{\sim}3.2HU$ (ROI $0.5\;cm^2$), $0.1{\sim}2.8HU$(ROI $3\;cm^2$) and $0.0{\sim}2.1$(ROI $6\;cm^2$). 3. The mean density of the normal thoracic tissues was highest in the bone algorithm, though there was no significant between 4 different reconstruction algorithms(p = 1.00).

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.336-344
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• 2005

In this paper, we extracted the contour of lung parenchyma on EBT images with the improved active contour model. The objects boundary in conventional active contour model can be extracted by controlling internal energy and external energy as energy minimizing form. However, there are a number of problems such as initialization and the poor convergence about concave part. Expecially, contour can not enter the concave region by discouraging characteristic about stretching and bending in internal energy. We controlled internal energy by moving local perpendicular bisector point of each control point in the contour and implemented the object boundary by minimizing energy with external energy The convergence of concave part could be efficiently implemented toward lung parenchyma region by this internal energy and both lung images for initial contour could also be detected by multi-detection method. We were sure this method could be applied detection of lung parenchyma region in medical image.

• Journal of radiological science and technology
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• v.15 no.1
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• pp.99-105
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• 1992

Visual evaluation of rib shadow and lung marking during high voltage chest radiography. The Purpose of this study is to improvement of visual discrimination of pulmonary structures on the conventional chest radiogram. The author prepared an artificial lung using an acryl plate, 8 cm in thickness, which is nearly equivalent to human lung, and 0.6 cm thickness of an aluminum plate for an artificial rib, and 0.5 cm of an acryl plate as a pulmonary vessel as well. And they were used as objects for experimental radiograms. This study performed with gradual increasing densities of film bases in the sequences of densities of 0.6, 0.9, 1.1 and 1.3. We made two combinations of images after multiple and regular cuts, with width of 1 cm, of 4 radiograms at the above mentioned densities of film bases. One image consisted of alternative combination of radiograms taken at densities of 0.6 and 1.3, and the other did at 0.9 and 1.1. The latter image provided better visual perception of pulmonary structures than the former. Experimental radiograms were also taken with 60 kV and 120 kV respectively. After careful evaluation and comparison to images taken on varieties of different densities with combinations and kV, the author had a conclusion that it is advisable to use a high kV X-ray which makes rib shadow subtle, for better visual delineation of pulmonary structures behind ribcage, eventhough contrast of pulmonary structures are decreased at high kV radiogram.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.