• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• Journal of the Korea Concrete Institute
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• v.20 no.4
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• pp.513-522
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• 2008

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.157-165
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• 2023

Ultrasound imaging uses sound waves of frequencies to cause physical actions such as reflection, absorption, refraction, and transmission at the edge between different tissues. Improvement is needed because there is a lot of noise due to the characteristics of the data generated from the ultrasound equipment, and it is difficult to grasp the shape of the tissue to be actually observed because the edge is vague. The edge enhancement method is used as a method to solve the case where the edge surface looks clumped due to a decrease in image quality. In this paper, as a method to strengthen the interface, the quality improvement was confirmed by strengthening the interface, which is the high-frequency part, in each image using an unsharpening mask and high boost. The mask filtering used for each image was evaluated by measuring PSNR and SNR. Abdominal, head, heart, liver, kidney, breast, and fetal images were obtained from Philips epiq5g and affiniti70g and Alpinion E-cube 15 ultrasound equipment. The program used to implement the algorithm was implemented with MATLAB R2022a of MathWorks. The unsharpening and high-boost mask array size was set to 3*3, and the laplacian filter, a spatial filter used to create outline-enhanced images, was applied equally to both masks. ImageJ program was used for quantitative evaluation of image quality. As a result of applying the mask filter to various ultrasound images, the subjective image quality showed that the overall contour lines of the image were clearly visible when unsharpening and high-boost mask were applied to the original image. When comparing the quantitative image quality, the image quality of the image to which the unsharpening mask and the high boost mask were applied was evaluated higher than that of the original image. In the portal vein, head, gallbladder, and kidney images, the SNR, PSNR, RMSE and MAE of the image to which the high-boost mask was applied were measured to be high. Conversely, for images of the heart, breast, and fetus, SNR, PSNR, RMSE and MAE values were measured as images with the unsharpening mask applied. It is thought that using the optimal mask according to the image will help to improve the image quality, and the contour information was provided to improve the image quality.

• Korean Journal of Materials Research
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• v.11 no.7
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• pp.556-561
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• 2001

We prepared a new a SOS(silicon-on-silicide) wafer pair which is consisted of Si(100)/1000$\AA$-NiSi Si (100) layers. SOS can be employed in MEMS(micro- electronic-mechanical system) application due to low resistance of the NiSi layer. A thermally evaporated $1000\AA$-thick Ni/Si wafer and a clean Si wafer were pre-mated in the class 100 clean room, then annealed at $300~900^{\circ}C$ for 15hrs to induce silicidation reaction. SOS wafer pairs were investigated by a IR camera to measure bonded area and probed by a SEM(scanning electron microscope) and TEM(transmission electron microscope) to observe cross-sectional view of Si/NiSi. IR camera observation showed that the annealed SOS wafer pairs have over 52% bonded area in all temperature region except silicidation phase transition temperature. By probing cross-sectional view with SEM of magnification of 30,000, we found that $1000\AA$-thick uniform NiSi layer was formed at the center area of bonded wafers without void defects. However we observed debonded area at the edge area of wafers. Through TEM observation, we found that $10-20\AA$ thick amourphous layer formed between Si surface and NiSix near the counter part of SOS. This layer may be an oxide layer and lead to degradation of bonding. At the edge area of wafers, that amorphous layer was formed even to thickness of $1500\AA$ during annealing. Therefore, to increase bonding area of Si NiSi ∥ Si wafer pairs, we may lessen the amorphous layers.

• Journal of the Korean Vacuum Society
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• v.17 no.6
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• pp.538-543
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• 2008

Recently a nano-scale diamond is possible to manufacture forms of powder(below 100 nm) by new processing of explosion or deposition method. Using a sintering of nano-scale diamond is possible to manufacture of grinding tools. We have need of a processing development of coated uniformly inorganic to prevent an abnormal grain growth of nano-crystal and bonding obstacle caused by sintering process. This paper, in order to improve the sintering property of nano-scale diamond, we coated ZnO thin films(thickness: $20{\sim}30\;nm$) in a vacuum by ALD(atomic layer deposition) Economically, in order to deposit ZnO all over the surface of nano-scale diamond powder, we used a new modified fluidized bed processing replaced mechanical vibration effect or fluidized bed reactor which utilized diamond floating owing to pressure of pulse(or purge) processing after inserted diamond powders in quartz tube(L: 20 mm) then closed quartz tube by porosity glass filter. We deposited ZnO thin films by ALD in closed both sides of quartz tube by porosity glass filter by ALD(precursor: DEZn($C_4H_{10}Zn$), reaction gas: $H_2O$) at $10^{\circ}C$(in canister). Processing procedure and injection time of reaction materials set up DEZn pulse-0.1 sec, DEZn purge-20 sec, $H_2O$ pulse-0.1 sec, $H_2O$ purge-40 sec and we put in operation repetitive 100 cycles(1 cycle is 4 steps) We confirmed microstructure of diamond powder and diamond powder doped ZnO thin film by TEM(transmission electron microscope) Through TEM analysis, we confirmed that diamond powder diameter was some $70{\sim}120\;nm$ and shape was tetragonal, hexagonal, etc before ALD. We confirmed that diameter of diamond powders doped ZnO thin film was some $70{\sim}120\;nm$ and uniform ZnO(thickness: $20{\sim}30\;nm$) thin film was successfully deposited on diamond powder surface according to brightness difference between diamond powder and ZnO.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.103-113
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• 2019

Spatial resolution is still one of the most important parameters for evaluating image quality. In this study, we propose an approach to evaluate spatial resolution and MTF(Modulation Transfer Function) using bar target and Siemens star chart as a part of quality evaluation for UAV images. To this end, images were taken with a fixed-wing eBee(Canon IXUS) at the flight height of 130m and 260m, and with a rotary-wing GD-800(SONY NEX-5N) at flight height of 130m, with a Phantom 4 pro(FC 6310) at flight height of 90m, respectively. Spatial resolution was measured on orthoimages produced from this data. Results show that the resolution measured on the Siemens star and bar target was accurately degraded in proportion to the flight height regardless of the cameras. In the words, the spatial resolution of images taken at the same altitude of 130m with the eBee(Canon IXUS) and the GD-800(SONY NEX-5N) equipped with different cameras was the same as 4.1cm, and that of the eBee(Canon IXUS) at 260m was 8.0cm. In addition, the resolution measured on the Siemens star was about 1~2cm lower than that of the bar target at every flight height. The general tendency was also found to be proportional to the flight height in the measurement of the ${\sigma}_{MTF}$ from MTF, which simultaneously represents the resolution and contrast information of the image. However, at the same altitude of 130m, the ${\sigma}_{MTF}$ of the GD-800(SONY NEX-5N) is 0.36 and the eBee(Canon IXUS) is 0.59, which shows that the GD-800(SONY NEX-5N) has better camera performance. It is expected that study results will contribute to the analysis of spatial resolution of UAV images and to improve the reliability of quality.