• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1349-1359
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• 2021

Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

• Journal of radiological science and technology
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• v.41 no.6
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• pp.561-566
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• 2018

Field-in-Field Technique is applied to the radiation therapy of breast cancer patients, and it is possible to compensate the difference in breast thickness and deliver uniform dose in the breast. However, there are several fields in the treatment field that result in a more complex dose delivery than a single field dose delivery. If the patient's respiration is irregular during the delivery of the dose by several fields and the change of respiration occurs, the dose distribution in the breast changes. Therefore, based on the computed tomography images of breast cancer patients, a human model was created by using a 3D printer (Builder Extreme 1000) to describe the volume in the same manner. A computerized tomography (CT) of the human body model was performed and a treatment plan of 260 cGy / fx was established using a 6-MV field-in-field technique using a computerized treatment planning system (Eclipse 13.6, Varian, USA). The distribution of the dose in the breast according to the change of the respiration was measured using a moving phantom at 0.1 cm, 0.3 cm, 0.5 cm amplitude, using a MOSOXIDE Silicon Field Effect Transistor (MOSFET, Best Medical, Canada) Were measured and compared. The distribution of dose in the breast according to the change of respiration showed similar value within ${\pm}2%$ in the movement up to 0.3 cm compared to the treatment plan. In this experiment, we found that the dose distribution in the breast due to the change of respiration when the change of respiration was increased was not much different from the treatment plan.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.439-446
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• 2019

In this paper, we propose a encryption method using C-MLCA and 1D CAT to secure medical image for efficiently. First, we generate a state transition matrix using a Wolfram rule and create a sequence of maximum length. By operating the complemented vector, it converts an existing sequence to a more complex sequence. Then, we multiply the two sequences by rows and columns to generate C-MLCA basis images of the original image size and go through a XOR operation. Finally, we will get the encrypted image to operate the 1D CAT basis function created by setting the gateway values and the image which is calculated by transform coefficients. By comparing the encrypted image with the original image, we evaluate to analyze the histogram and PSNR. Also, by analyzing NPCR and key space, we confirmed that the proposed encryption method has a high level of stability and security.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.187-193
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• 2021

Purpose: This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. Materials and Methods: Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process. Results: Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients. Conclusion: Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1255-1261
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• 2020

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms.

• Imaging Science in Dentistry
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• v.51 no.4
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• pp.383-388
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• 2021

Purpose: The aim of this study was to evaluate the root canal morphology of mesial roots of mandibular first molars. Materials and Methods: Forty extracted mandibular first molars were used in this study. The morphological examination of root canals was conducted in accordance with the Vertucci classification using micro-computed tomography (micro-CT). Any aberrant root canal configurations not included in the Vertucci classification were recorded, and their frequency was established using descriptive statistics. Intra-observer reliability was assessed using the Wilcoxon signed-rank test, while inter-observer reliability was assessed using the Cohen kappa test. Significance was evaluated at the P<0.05 level. Results: The mesial roots of mandibular first molars had canal configurations of type I (15%), type II (7.5%), type III (25%), type IV (10%), type V (2.5%), type VI (7.5%), and type VII (7.5%). The images showed 10 (25%) additional configuration types that were not included in the Vertucci classification. These types were 1-3-2-3, 1-2-3-2-3, 2-3-1, 2-3, 1-2-3-1, 2-1-2-3, 3-2-1, 1-2-3-1, 2-3-2-3, and 1-2-1-2-1. The intra-observer differences were not statistically significant(P>0.05) and the kappa value for inter-observer agreement was found to be 0.957. Conclusion: Frequent variations were detected in mesial roots of mandibular first molars. Clinicians should take into consideration the complex structure of the root canal morphology before commencing root canal treatment procedures to prevent iatrogenic complications. Micro-CT was a highly suitable method to provide accurate 3-dimensional visualizations of root canal morphology.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.