• Applied Microscopy
• /
• v.40 no.1
• /
• pp.47-51
• /
• 2010

The capability of real-time observation in TEM is quite useful to study dynamic phenomena of materials in a certain variable ambience. In performing the experiment, the choice of video recording program is an important factor to obtain high quality of movie streaming. Window Movie Maker (WMM) is generally recommended as a default video recording program if one uses "DV Capture" function in DigitalMicrograph$^{TM}$ (DM) software. However, the image quality does not often satisfy the condition for high-resolution microscopic analysis since the severe information loss in the final result occurs during the conversion process. As a good candidate to overcome this problem, Virtual-Dub is highly recommended since the information loss can be minimized through the streaming process. In this report, we demonstrated how useful VirtualDub works in a high-resolution movie recording. Quantitative comparison of the information quality between the images recorded by each software, WMM and VirtualDub, was carried out based on histogram analysis. As a result, the image recorded by VirtualDub was improved ~13% in brightness and ~122% in contrast compared with the image obtained by WMM at the same imaging condition. Remarkably, the gray gradation (meaning an amount of information) becomes wider up to ~115% than that of the WMM result.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.2
• /
• pp.167-171
• /
• 2014

Using chemical vapor deposition(CVD), the synthesis of graphene was performed on poly and single crystalline Cu substrates. The growth behavior of graphene and its characterization were shown utilizing the optical microscopic image and its image analysis. As a result in the analysis of graphene growth, it was found out the graphene is growing always in particular direction in relation to the crystalline direction of a single grain in polycrystalline Cu substrate. With the image analysis it was possible to show the characterization of graphene, such as the growth direction and the number of layers showing single, double and triple layers, within the neighboring single grains in polycrystalline Cu. In addition, the relatively large area of graphene with about $3mm^2$ on Cu(111) having high quality, single layer, and single crystalline was shown along with its characterization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.5
• /
• pp.373-383
• /
• 2015

This paper proposes a decision support system to recognizing retinal diseases. This paper uses a smartphone platform and cloud computing as the base of the system. A microscopic lens is attached int' the smartphone camera to capture the user retinal image for recognizing the user's retinal condition. An application is assembled in computer and then installed in to the smartphone. The application role is to connect between the system in smartphone and system in cloud, the application will send the retinal image to the cloud system to be classified. The paper uses OCFE (optimized classifier based on feature elimination) algorithm as the classifier. The retinal image is trained using combination of two ophthalmology databases DIARETDB1 v2.1 and STARE. Therefore, this system average accuracy is 88%, while the average error rate is 12%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.77-82
• /
• 2021

There are three types of urinalysis: physical test, chemical test, and microscopic test. Among these, the chemical urinalysis is an easily accessible method of the general public to compare the chemical reaction of urinalysis strip with a standard colorimetric table by sight or purchase the portable urinalysis machine separately. Currently, with the popularization of smartphone, research on the urinalysis service using smartphone is increasing. The urinalysis screening application is one of the urinalysis services using a smartphone. However, the RGB values of the urinalysis pad taken by the urinalysis screening application have large deviations due to the effect of lighting. Deviation of RGB value debases the accuracy of urinalysis discrimination. Therefore, in this paper, the accuracy of urinaylsis pad image discrimination is improved through CNN after classifying urinalysis strips taken by the urinalysis screening application based on smartphone by urinalysis pad items. Urinalysis strip was taken from various backgrounds to generate CNN image, and urinalysis discrimination was analyzed using the ResNet-50 CNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.134-136
• /
• 2021

Research related to early diagnosis of breast cancer using artificial intelligence algorithms has been actively conducted in recent years. Although various algorithms that classify breast cancer based on a few publicly available ultrasound breast cancer images have been published, these methods show various limitations such as, processing speed and accuracy suitable for the user's purpose. To solve this problem, in this paper, we propose a multi-stage transfer learning where ResNet model trained on ImageNet is transfer learned to microscopic cancer cell line images, which was again transfer learned to classify ultrasound breast cancer images as benign and malignant. The images for the experiment consisted of 250 breast cancer ultrasound images including benign and malignant images and 27,200 cancer cell line images. The proposed multi-stage transfer learning algorithm showed more than 96% accuracy when classifying ultrasound breast cancer images, and is expected to show higher utilization and accuracy through the addition of more cancer cell lines and real-time image processing in the future.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.1
• /
• pp.76-81
• /
• 2023

To distinguish cancer cells from normal cells, H&E (Hematoxylin & Eosin) staining is required. Pathological staining requires a lot of money and time. Recently, a digital dyeing method has been introduced to reduce such cost and time. In this paper, we propose a novel digital pathology algorithms. The first algorithm is the Pair method. This method learns the dyed phase image and unstained amplitude image taken by FPM (Fourier Ptychographic Microscopy) and converts it into a dyed amplitude image. The second algorithm is the unpair method. This method use the stained and unstained fluorescence microscopic images for modeling. In this study, digital staining was performed using a generative adversarial network (GAN). From the experimental results, we noticed that both the pair and unpair algorithms shows the excellent performance.

• Geotechnical Engineering
• /
• v.14 no.1
• /
• pp.109-126
• /
• 1998

The use of fly ash as a contaminant barrier material was studied. Mixing ratio of fly ash to bentonite to meet the requirements for landfill liners was determined. The hydraulic behavior exhibited by the fly ash-bentonite liner and the effects of CaO were investigated through hydraulic conductivity tests under various conditions and microscopic analyses including XRD, SEM, helium porosimetry, and image analysis. The hydraulic conductivity of compacted fly ash decreased with the addition of bentonite, which was due mainly to the expanding of bentonite and partly to the filling of voids by chemical reaction products among constituents of the artificial liner. Because of insufficient CaO content, and rich in content but low-reactive $SiO_2$ contained in the fly ashes used, pozzolanic reaction and resulting effects in the artificial liner were not significant. The reactions among constituting materials and their resulting effects on hydraulic conductivity were controlled not by the apparent amounts of each constituent, but by reaction activities of the materials in the artificial liner.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.3
• /
• pp.38-50
• /
• 2023

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

• Korean Journal of Optics and Photonics
• /
• v.10 no.1
• /
• pp.21-26
• /
• 1999

The technique for calibrating standard particles larger than 1 $\mu$m by using an optical array sizing method has been established. The system consists of an optical microscope with transmission light source, a CCD camera, and a computer equipped with a frame grabber. The center distance between two spheres located at both ends of a row which consists of N spheres is measured in terms of pixel numbers, and divided by (N-1) to obtain the average pixel numbers per particle. This value is multiplied by length conversion constant, which has been determined in advance, to obtain the mean diameter of polystylene spheres. The length conversion constant is found from the microscopic image of calibrated standard stage micrometer plate. In order to reduce error in finding center positions of the scale and particles in the image, a software filter which dilates bright (or dark) object has been used. Spheres having nominal size of 1, 2, 3, 5, and 10$\mu$m were measured, and the maximum deviation of the measured mean diameter valus from their certified values was 0.7%. Standard particles which is larger than 1 $\mu$m can be calibrated by this method with measuredment uncertainy (k=2) lees than 1.53%.

• Imaging Science in Dentistry
• /
• v.45 no.3
• /
• pp.141-145
• /
• 2015

Purpose: This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods: Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections $5{\mu}m$ in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (${\alpha}=0.05$). Results: Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion: The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy.