• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.273-280
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• 2013

This paper presents novel OCS-LBP (Oriented Center Symmetric Local Binary Patterns) based on orientation of pixel gradient and image retrieval system based on BoF (Bag-of-Feature) and random forest classifier. Feature vectors extracted from training data are clustered into code book and each feature is transformed new BoF feature using code book. BoF features are applied to random forest for training and random forest having N classes is constructed by combining several decision trees. For testing, the same OCS-LBP feature is extracted from a query image and BoF is applied to trained random forest classifier. In contrast to conventional retrieval system, query image selects similar K-nearest neighbor (K-NN) classes after random forest is performed. Then, Top K similar images are retrieved from database images that are only labeled K-NN classes. Compared with other retrieval algorithms, the proposed method shows both fast processing time and improved retrieval performance.

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.39-50
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• 1999

It is complicate and time-consuming process to classify a multi-band satellite imagery according to the application. In addition, classification rate sensitively depends on the selection of training data set and features in a supervised classification process. This paper introduced a classification network adopting a fuzzy-based $\gamma$-model in order to select a training data set and to extract feature which highly contribute to an actual classification. The features used in the classification were gray-level histogram, textures, and NDVI(Normalized Difference Vegetation Index) of target imagery. Moreover, in order to minimize the errors in the classification network, the Gradient Descent method was used in the training process for the $\gamma$-parameters at each code used. The trained parameters made it possible to know the connectivity of each node and to delete the void features from all the possible input features.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.97-105
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• 2008

The various mutations of the malicious codes are fast generated on the network. Also the behaviors of them become intelligent and the damage becomes larger step by step. In this paper, we suggest the method to select the useful measures for the detection of the codes. The method has the advantage of shortening the detection time by using header data without payloads and uses connection data that are composed of TCP/IP packets, and much information of each connection makes use of the measures. A naive estimator is applied to the probability distribution that are calculated by the histogram estimator to select the specific measures among 80 measures for the useful detection. The useful measures are then selected by using relative entropy. This method solves the problem that is to misclassify the measure values. We present the usefulness of the proposed method through the result of the detection experiment using the detection patterns based on the selected measures.

• Progress in Medical Physics
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• v.34 no.2
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• pp.15-22
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• 2023

This study compared the dose calculated using the electron Monte Carlo (eMC) dose calculation algorithm employing the old version (eMC V13.7) of the Varian Eclipse treatment-planning system (TPS) and its newer version (eMC V16.1). The eMC V16.1 was configured using the same beam data as the eMC V13.7. Beam data measured using the VitalBeam linear accelerator were implemented. A box-shaped water phantom (30×30×30 cm³) was generated in the TPS. Consequently, the TPS with eMC V13.7 and eMC V16.1 calculated the dose to the water phantom delivered by electron beams of various energies with a field size of 10×10 cm². The calculations were repeated while changing the dose-smoothing levels and normalization method. Subsequently, the percentage depth dose and lateral profile of the dose distributions acquired by eMC V13.7 and eMC V16.1 were analyzed. In addition, the dose-volume histogram (DVH) differences between the two versions for the heterogeneous phantom with bone and lung inserted were compared. The doses calculated using eMC V16.1 were similar to those calculated using eMC V13.7 for the homogenous phantoms. However, a DVH difference was observed in the heterogeneous phantom, particularly in the bone material. The dose distribution calculated using eMC V16.1 was comparable to that of eMC V13.7 in the case of homogenous phantoms. The version changes resulted in a different DVH for the heterogeneous phantoms. However, further investigations to assess the DVH differences in patients and experimental validations for eMC V16.1, particularly for heterogeneous geometry, are required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.6-8
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• 2022

Many systems that use images through object recognition using deep learning have provided various solutions beyond the existing methods. Many studies have proven its usability, and the actual control system shows the possibility of using it to make people's work more convenient. Many studies have proven its usability, and actual control systems make human tasks more convenient and show possible. However, with hardware-intensive performance, the development of models is facing some limitations, and the ease with the use and additional utilization of many unupdated models is falling. In this paper, we propose how to increase utilization and accuracy by providing additional information on the emotional regions of colors and objects by utilizing learning and weights from HSV color histograms of local image data recognized after conventional stereotyped object recognition results.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.409-414
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• 2007

In spite of many studies of the acoustic field visualization by using sonoluminescence phenomena, the visualization method has not been used widely because it needs high acoustic intensity to get the luminescence intensity enough to observe. Recently, the digital camera with high resolution and big memory makes it possible to get the digital image data even though the brightness of the image is too weak to observe with naked eyes. In this study we investigated the variation of sonoluminescence intensity with the acoustic intensity from an ultrasonic transducer. From this result, the inverse function, which makes the tendency of the variation to linear, was obtained. Using the order of the inverse function, we can expect a matching function. Applying the matching function to digital image data, the distribution of the histogram could be controlled appropriately and the image from relatively weak acoustic intensity could be enhanced by the method.

• The Journal of Korean Society for Radiation Therapy
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• v.19 no.2
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• pp.113-122
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• 2007

Purpose: We proposed the method using dose-volume Histogram index to compare prospective plan trials in tomotherapy planning optimization. Materials and Methods: For 3 patients in cranial region, thorax and abdominal region, we acquired computed tomography images with PQ 5000 in each case. Then we delineated target structure and normal organ contour with pinnacle Ver 7.6c, after transferred each data to tomotherapy planning system (hi-art system Ver 2.0), we optimized 3 plan trials in each case that used differ from beam width, pitch, importance. We analyzed 3 plan trials in each region with isodose distribution, dose-volume histogram and dose statistics. Also we verified 3 plan trials with specialized DVH-indexes that is dose homogeneity index in target organ, conformity index around target structure and dose gradient index in non-target structures. Results: We compared with the similarity of results that the one is decide the best plan trial using isodose distribution, dose volume histogram and dose statistics, and the another is using DVH-indexes. They all decided the same plan trial to better result in each case. Conclusion: In some of case, it was appeared a little difference of results that used to DVH-index for comparison of plan trial in tomotherapy by special goal in it. But because DVH-index represented both dose distribution in target structure and high dose risk about normal tissue, it will be reasonable method for comparison of many plan trials before the tomotherapy treatments.

• Radiation Oncology Journal
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• v.19 no.4
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• pp.319-326
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• 2001

Purpose : Planning target volume (PTV) for tumors in abdomen or thorax includes enough margin for breathing-related movement of tumor volumes during treatment. Depending on the location of the tumor, the magnitude of PTV margin extends from 10 mm to 30 mm, which increases substantial volume of the irradiated normal tissue hence, resulting in increase of normal tissue complication probability (NTCP). We developed a simple and handy method which can reduce PTV margins in patients with liver tumors, respiratory motion reduction device (RRD). Materials and methods : For 10 liver cancer patients, the data of internal organ motion were obtained by examining the diaphragm motion under fluoroscope. It was tested for both supine and prone position. A RRD was made using MeV-Green and Styrofoam panels and then applied to the patients. By analyzing the diaphragm movement from patients with RRD, the magnitude of PTV margin was determined and dose volume histogram (DVH) was computed using AcQ-Plan, a treatment planning software. Dose to normal tissue between patients with RRD and without RRD was analyzed by comparing the fraction of the normal liver receiving to $50\%$ of the isocenter dose. DVH and NTCP for normal liver and adjacent organs were also evaluated. Results : When patients breathed freely, average movement of diaphragm was $12{\pm}1.9\;mm$ in prone position in contrast to $16{\pm}1.9\;mm$ in supine position. In prone position, difference in diaphragm movement with and without RRD was $3{\pm}0.9\;mm$ and 12 mm, respectively, showing that PTV margins could be reduced to as much as 9 mm. With RRD, volume of the irradiated normal liver reduced up to $22.7\%$ in DVH analysis. Conclusion : Internal organ motion due to breathing can be reduced using RRD, which is simple and easy to use in clinical setting. It can reduce the organ motion-related PTV margin, thereby decrease volume of the irradiated normal tissue.

• Radiation Oncology Journal
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• v.15 no.3
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• pp.243-249
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• 1997

Purpose : In radiation therapy, NTCF is very importart indicator of selecting the optimal treatment plan. In our study, we tried to find out usefullness of NTCP in lung cancer by comparng the incidence of radiation pneumonitis with NTCP. Materials and Methods : From August 1993 to December 1994, thirty six patients with locally advanced non=small cell lung cancer were treated by concurrent chemoradiation therapy. Total dose of radiation therapy was 6480cGy (120cGy, bid) and chemotherapeutlc agents were mitomycin C. vinblastion, cisplatin (2 cycles, 4 weeks interval). We evaluated the development of raniation pneumonitis by CT scan, chest x-rar and clinical symptoms. We used grading system of South Western Oncology Group (SWOG) for radiation pneumanitis. Dose Volume Histograms (DVH) were analyzed for ipsilateral and whole lung, Non uniform DVH was translated to uniform DVH by effective volume method. With these data, we calculated NTCP for ipsilateral and whole lung. Finally we compared the clinical results to NTCP. Results : Eight of thrity six patients developed radiation pneumonitis. Of these 8 patients , 6 had grade I severity and 2 had grade II. The average NTCP value cf the patients who showed radiation pneumonitis was significantly higher than that uf the patients without pneumonitis $(66\%\;vs.\;26.4\%)$. But the results of pulmonary function test was not correlated with NTCP. Conclusion : NTCP of lung is very good indicator for selecting rival treatment planning in lung cancer. According to the results of NTCP, it may be possible to adjust target volume and optimize target dose. In the near future, we are going to anaiyze the effect of hyperfractionation and concurrent chemotherapy in addition to NTCP.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.