• Journal of Veterinary Science
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• v.22 no.5
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• pp.65.1-65.12
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• 2021

Background: To assess the normal retina of the pigeon eye using spectral domain optical coherence tomography (SD-OCT) and establish a normative reference. Methods: Twelve eyes of six ophthalmologically normal pigeons (Columba livia) were included. SD-OCT images were taken with dilated pupils under sedation. Four meridians, including the fovea, optic disc, red field, and yellow field, were obtained in each eye. The layers, including full thickness (FT), ganglion cell complex (GCC), thickness from the retinal pigmented epithelium to the outer nuclear layer (RPE-ONL), and from the retinal pigmented epithelium to the inner nuclear layer (RPE-INL), were manually measured. Results: The average FT values were significantly different among the four meridians (p < 0.05), with the optic disc meridian being the thickest (294.0 ± 13.9 ㎛). The average GCC was thickest in the optic disc (105.3 ± 27.1 ㎛) and thinnest in the fovea meridian (42.8 ± 15.3 ㎛). The average RPE-INL of the fovea meridian (165.5 ± 18.3 ㎛) was significantly thicker than that of the other meridians (p < 0.05). The average RPE-ONL of the fovea, optic disc, yellow field, and red field were 91.2 ± 5.2 ㎛, 87.7 ± 5.3 ㎛, 87.6 ± 6.5 ㎛, and 91.4 ± 3.9 ㎛, respectively. RPE-INL and RPE-ONL thickness of the red field meridian did not change significantly with measurement location (p > 0.05). Conclusions: Measured data could be used as normative references for diagnosing pigeon retinopathies and further research on avian fundus structure.

• The Korean Fashion and Textile Research Journal
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• v.23 no.4
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• pp.527-535
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• 2021

In this study, electroconductive textiles were developed by screen-printing technology using a complex solution of PEDOT:PSS/AgNW on a polylactic acid nanofiber web. A performance evaluation was then conducted to utilize this electroconductive textile as a signal transmission line. To obtain highly conductive electroconductive textiles, this study sought to determine the optimal mixing ratio of PEDOT:PSS/AgNW. Sheet resistance was measured to evaluate the electrical properties of electroconductive textiles, Finite element-scanning electron microscopy images were then used to examine surface properties, and Fourier transform-infrared analysis was performed to evaluate chemical properties. The signal waveform characteristics of the electroconductive textile were observed using a signal generator and an oscilloscope. Radio-frequency characteristics were then evaluated to confirm frequency range, and bending tests were conducted to evaluate durability. The signal transmission lines produced in this study had a sheet resistance value of 3.30 ?/sq, and signal transmission performance was evaluated to observe that the input value of the voltage was nearly identical to the output value. In addition, S21 analysis confirmed that it was available in the frequency domain up to 35 MHz. The performances of the transmission lines were maintained after 100, 200, 500, and 1,000 repeated bending tests, and sufficient durability was confirmed.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.47 no.5
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• pp.382-387
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• 2021

Objectives: Zygomatic complex (ZMC) fractures comprise up to 40% of all facial fractures. Misaligned bone fragments and misplaced fixation hardware traditionally detected postoperatively on plain radiographs of the skull might require re-operation. The intraoperative O-Arm (Medtronic, USA) is a three-dimensional (3D) computed tomographic imaging system. Materials and Methods: This retrospective single-center study evaluated the utility of O-Arm scanning during corrective surgeries for ZMC and zygomatic arch (ZA) fractures from 2018 to 2020. Three females and 16 males (mean age, 31.52 years; range, 22-48 years) were included. Fracture instability (n=6) and facial deformity (n=15) were the most frequent indications for intraoperative 3D O-Arm scan. Results: The images demonstrated that all fracture lines were properly reduced and fixed. Another scan performed at the end of the fixation or reduction stage, however, revealed suboptimal results in five of the 19 cases, and further reduction and fixation of the fracture lines were required. Conclusion: Implementation of an intraoperative O-Arm system in ZMC and ZA fracture surgeries assists in obtaining predictable and accurate results and obviates the need for revision surgeries. The device should be considered for precise operations such as ZMC fracture repairs.

• 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.