• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3816-3823
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• 2022

This work analyzed the dosimetric difference between the intensity modulated radiotherapy (IMRT), partial/single/double-arc volumetric modulated arc therapy (PA/SA/DA-VMAT) techniques in treatment planning for treating more than one target of lung cancer at different isocenters. IMRT and VMAT plans at different isocenters were created systematically using a Harold heterogeneous lung phantom. The conformity index (CI), homogeneity index (HI), gradient index (GI), dose-volume histogram and mean and maximum dose of the PTV were calculated and analyzed. Furthermore, the dose-volume histogram and mean and maximum doses of the OARs such as right lung, contralateral lung and non GTV were determined from the plans. The IMRT plans showed the superior target dose coverage, higher mean and maximum values than other VMAT techniques. PA-VMAT technique shows more lung sparing and DA-VMAT increases the V_5/10/20 values of contralateral lung than other VMAT and IMRT techniques. The IMRT technique achieves highly conformal dose distribution to the target than other VMAT techniques. Comparing to the IMRT plans, the higher V_5/10/20 and mean lung dose were observed in the contralateral lung in the DA-VMAT.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.193-199
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• 2018

In this research, 15 patients were diagnosed with 1.5T and 3.0T MRI instruments (Philips, Medical System, Achieva) to minize Ferromagnetic artifact and find the optimized Tesla. Based on the theory that the 3.0T, when compared to 1.5T, show relatively high signal-to-ratio(SNR), Scan time can be shortened or adjust the image resolution. However, when using the 3.0T MRI instruments, various artifact due to the magnetic field difference can degrade the diagnostic information. For the analysis condition, area of interest is set at the background of the T1, T2 sagittal image followed by evaluation of L3, L4, L5 SNR, length of 3 parts with Ferromagnetic artifact, and Histogram. The validity evaluation was performed by using the independent t test. As a result, for the SNR evaluation, mere difference in value was observed for L3 between 1.5T and 3.0T, while big differences were observed for both L4, and L5(p<0.05). Shorter length was observed for the 1.5T when observing 3 parts with Ferromagnetic artifact, thus we can conclude that 3.0T can provide more information on about peripheral tissue diagnostic information(p<0.05). Finally, 1.5T showed higher counts values for the Histogram evaluation(p<0.05). As a result, when we have compared the 1.5T and 3.0T with SNR, length of Ferromagnetic artifact, Histogram, we believe that using a Low Tesla for Spine MRI test can achieve the optimal image information for patients with disk operation like PLIF, etc. in the past.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.84-93
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• 1993

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for the shape distortions in handwritten characters. In this paper, we review these nonlinear shape normalization methods from the two points of view : feature projection and feature density equalization. The former makes feature projection histogram by projecting a certain feature at each point of input image into horizontal-or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. A systematic comparison of these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity and measure of variation. Then, we present the result of quantitative evaluation of each method based on these criteria for a large variety of handwritten Hangul syllables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1301-1307
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• 2001

Video retrieval system must offer representation frame list to do to do play from point that user wants. Representation frame list can get though cut detection point exactly. This paper dismembers frame to fixed block to cut detection point, and compare same block histogram cost of next time frame. If result that compare does not exceed thresold, detect next frame to cutting.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.45-53
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• 2014

This article describes a two wheelers detection system riding on people based on modified histogram of oriented gradients (HOG) for vision based intelligent vehicles. These features used correlation coefficient parameter are able to classify variable and complicated shapes of a two wheelers according to different viewpoints as well as human appearance. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. In this paper, we propose an evolutionary method trained part-based models to classify multiple view-based detection: frontal, rear and side view (within $60^{\circ}C$). Our experimental results show that a two wheelers riding on people detection system based on proposed approach leads to higher detection accuracy rate than traditional features.

• The Journal of Information Technology
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• v.12 no.4
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• pp.25-32
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. This paper is appropriate for the embedded environment using local gradient histogram embedded system using iris feature extraction methods have implement. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.

• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.53-57
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• 2007

In this paper, a new content-based color image retrieval method is proposed, in which both the color content and the spatial relationship of image have been taken into account. In order to represent the spatial distribution information of image, a disorder matrix, which has the invariance to the rotation and translation of the image content, has been designed. This is based on multi-resolution color-spatial information. We present our algorithm in the following section, and then verified the search results with comparison to other methods, such as color histogram, wavelet histogram, correlogram and wavelet correlogram. Experimental results with various types of images show that the proposed method not only achieves a high image retrieval performance but also improve the retrieval precision.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.259-264
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• 2007

According to the development and wide use of broad band internet etc., diverse application technologies using large capacity data such as images have been progressed and in these systems, for accurate acquisition and precise applications of an original signal, the degradation phenomenon generated in the transmission process etc. should be removed. Noises have become known as the main cause of the degradation phenomenon and especially Gaussian noise represents characteristics occurring dependently in image signals and degrades detail information such as edge. In this paper, we removed Gaussian noise using a subdivided nonlinear function according to a threshold value and analyzed the histogram acquired from an edge image to establish a threshold value adaptively, and strengthened detail information of image by using the postprocessing. In simulation results, the proposed method represented excellent performance from comparison of MSE with existing methods.