• Journal of Yeungnam Medical Science
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• v.37 no.2
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• pp.90-97
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• 2020

Background: To minimize damage to the ovarian reserve, it is necessary to evaluate the follicular density in the ovarian tissue surrounding endometriosis on preoperative imaging. The purpose of the present study was to evaluate the usefulness of subtraction pelvic magnetic resonance imaging (MRI) to detect ovarian reserve. Methods: A subtracted T1-weighted image (^subT1WI) was obtained by subtracting unenhanced T1WI from contrast-enhanced T1WI (^ceT1WI) with similar parameters in 22 patients with ovarian endometriosis. The signal-to-noise ratio (SNR) in ovarian endometriosis, which was classified into the high signal intensity and iso-to-low signal intensity groups on the T2-weighted image, was compared to that in normal ovarian tissue. To evaluate the effect of contrast enhancement, a standardization map was obtained by dividing ^subT1WI by ^ceT1WI. Results: On visual assessment of 22 patients with ovarian endometriosis, 16 patients showed a high signal intensity, and 6 patients showed an iso-to-low signal intensity on T1WI. Although SNR in endometriosis with a high signal intensity was higher than that with an iso-to-low signal intensity, there was no difference in SNR after the subtraction (13.72±77.55 vs. 63.03±43.90, p=0.126). The area of the affected ovary was smaller than that of the normal ovary (121.10±22.48 vs. 380.51±75.87 ㎟, p=0.002), but the mean number of pixels in the viable remaining tissue of the affected ovary was similar to that of the normal ovary (0.53±0.09 vs. 0.47±0.09, p=0.682). Conclusion: The subtraction technique used with pelvic MRI could reveal the extent of endometrial invasion of the normal ovarian tissue and viable remnant ovarian tissue.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.443-451
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• 2017

Mammography is commonly used for screening early breast cancer. However, mammographic images, which depend on the physical properties of breast components, are limited to provide information about whether a lesion is malignant or benign. Although a dual-energy subtraction technique decomposes a certain material from a mixture, it increases radiation dose and degrades the accuracy of material decomposition. In this study, we simulated a breast phantom using attenuation characteristics, and we proposed a technique to enable the accurate material decomposition by applying weighting factors for the dual-energy mammography based on a photon-counting detector using a Monte Carlo simulation tool. We also evaluated the contrast and noise of simulated breast images for validating the proposed technique. As a result, the contrast for a malignant tumor in the dual-energy weighted subtraction technique was 0.98 and 1.06 times similar than those in the general mammography and dual-energy subtraction techniques, respectively. However the contrast between malignant and benign tumors dramatically increased 13.54 times due to the low contrast of a benign tumor. Therefore, the proposed technique can increase the material decomposition accuracy for malignant tumor and improve the diagnostic accuracy of mammography.

• Journal of Korean Neurosurgical Society
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• v.40 no.4
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• pp.277-280
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• 2006

Extracerebral cavernous hemangiomas are rare vascular tumors that are very difficult to remove because of severe intraoperative bleeding. We report a case of 57-year-old male with extracerebral cavernous hemangioma with review of 126 cases in the literature. Patient presented with blurred vision, diplopia, numbness on the left side of his face. Magnetic resonance imaging revealed a well defined mass of $3{\times}4{\times}3cm$ size with heterogenous iso-or hypointensity on T1-weighted image showing strong homogenous contrast enhancement and marked hyperintensity on T2-weighted image. Digital subtraction angiography[DSA] revealed a faint tumor blush by feeders from the left internal carotid artery[ICA] and left external carotid artery[ECA] in the delayed phase. Even with profuse intratumoral bleeding, near total removal was achieved. In addition to preoperative neurologic deficits such as ophthalmoplegia, facial numbness in the V1-2 dermatomes, ptosis appeared postoperatively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.779-785
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• 2007

Moving object detection is very important for video surveillance in modern days. In special case, we can categorize motions into two types-salient and non-salient motion. In this paper, we first calculate temporal difference image for extract moving objects and adapt to dynamic environments and next, we also propose a new algorithm to detect salient motion information in complex environment by combining temporal difference image and binary block image which is calculated by motion vector using the newest MPEG-4 and EPZS, and it is very effective to detect objects in a complex environment that many various motions are mixed.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.359-366
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• 2016

The normal tissue in radiation therapy, to minimize radiation, it is most important to maximize local tumor control rates in intensive research the exact dose to the tumor sites. Therefore, the initial, therapist accuracy of detecting movement of the patient fatigue therapist has been a problem that is weighted down directly. Also, by using a web camera, a difference value between the image to be updated to the reference image is calculated, if the result exceeds the reference value, using the system for determining the motion has occurred. However, this system, it is not possible to quantitatively analyze the movement of the patient, the background is changed when moving the treatment bed in the co-therapeutic device was not able to sift the patient. In this paper, using a alpah(${\alpha}$) filter index is an attempt to solve these limitations points, quantifies the movement of the patient, by separating a background image of the patient and treatment environment, and movement of the patient during treatment It senses only, it was possible to reduce the problems due to patient movement.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1095-1102
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• 2014

In this paper, We proposed the approach of GPU and motion vector to analysis the Human activity in real-time surveillance system. The most important part, that is detect blob(human) in the foreground. We use to detect Adaptive Gaussian Mixture, Weighted subtraction image for salient motion and motion vector. And then, We use motion vector for human activity analysis. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}. We created approximately 300 conditions for the simulation. As a result, We showed a high success rate about 86~98%. The results also showed that the high resolution experiment by the proposed GPU-based method was over 10 times faster than the cpu-based method.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.105-113
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• 2016

Purpose: Susceptibility vessel sign (SVS) on gradient echo image, which is caused by MR signal loss due to arterial thrombosis, has been reported in acute middle cerebral artery (MCA) infarction. However, the reported sensitivity and diagnostic accuracy of SVS have been variable. Susceptibility-weighted imaging (SWI) is a newly developed MR sequence. Recent studies have found that SWI may be useful in the field of cerebrovascular diseases, especially for detecting the presence of prominent veins, microbleeds and the SVS. The purpose of this study was to evaluate the diagnostic values of SWI for the detection of hyperacute MCA occlusion. Materials and Methods: Sixty-nine patients (37 males, 32 females; 46-89 years old [mean, 69.1]) with acute stroke involving the MCA territory underwent MR imaging within 6 hours after the symptom onset. MR examination included T2, FLAIR (fluid-attenuated inversion recovery), DWI, SWI, PWI (perfusion-weighted imaging), contrast-enhanced MR angiography (MRA) and contrast-enhanced T1. Of these patients, 28 patients also underwent digital subtraction angiography (DSA) within 2 hours after MR examination. Presence or absence of SVS on SWI was assessed without knowledge of clinical, DSA and other MR imaging findings. Results: On MRA or DSA, 34 patients (49.3%) showed MCA occlusion. Of these patients, SVS was detected in 30 (88.2%) on SWI. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of SWI were 88.2%, 97.1%, 96.8%, 89.5% and 92.8%, respectively. Conclusion: SWI was sensitive, specific and accurate for the detection of hyperacute MCA occlusion.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.