• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.322-330
• /
• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.6
• /
• pp.30-43
• /
• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Proceedings of the Korea Contents Association Conference
• /
• 2018.05a
• /
• pp.459-460
• /
• 2018

In this paper, we present a data augmentation method that generates synthetic plant disease images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation techniques to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of 2789 images of tomato plant diseases (Gray mold, Canker, Leaf mold, Plague, Leaf miner, Whitefly etc.).

• Journal of Broadcast Engineering
• /
• v.25 no.1
• /
• pp.1-12
• /
• 2020

In the medical field, numerical imbalance of data due to differences in disease prevalence is a common problem. It reduces the performance of a artificial intelligence network, leading to difficulties in learning a network with good performance. Recently, generative adversarial network (GAN) technology has been introduced as a way to address this problem, and its ability has been demonstrated by successful applications in various fields. However, it is still difficult to achieve good results in solving problems with performance degraded by numerical imbalances because the image resolution of the previous studies is not yet good enough and the structure in the image is modeled locally. In this paper, we propose a multi-scale conditional generative adversarial network based on attention mechanism, which can produce high resolution images to solve the numerical imbalance problem of chest X-ray image data. The network was able to produce images for various diseases by controlling condition variables with only one network. It's efficient and effective in that the network don't need to be learned independently for all disease classes and solves the problem of long distance dependency in image generation with self-attention mechanism.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.2
• /
• pp.26-37
• /
• 2003

Unlike a medical doctor, a dentist performs all the tasks necessary for diagnosis and treatment of a disease all by himself. To increase the diagnostic accuracy, dentists need an efficient working environment providing much more integrated information of clinical data and radiographic image. In this paper, we propose an integrated environment for the dental hospital. It provides paperless and filmless hospital environment by integrating seamlessly three major operations for the dental hospital including patient record generation and management, clinical image acquisition and analysis, and treatment planning and simulation. This system also allows clinicians to provide more predictable dental care for the patients by supporting instant access to all the clinical data and quantitative data analysis.

• Journal of Sensor Science and Technology
• /
• v.27 no.5
• /
• pp.280-293
• /
• 2018

The conversion of an invisible thermal radiation pattern of an object into a visible image using infrared (IR) thermal technology is very useful to understand phenomena what we are interested in. Although IR thermal images were originally developed for military and space applications, they are currently employed to determine thermal properties and heat features in various applications, such as the non-destructive evaluation of industrial equipment, power plants, electricity, military or drive-assisted night vision, and medical applications to monitor heat generation or loss. Recently, IR imaging-based monitoring systems have been considered for application in agricultural, including crop care, plant-disease detection, bruise detection of fruits, and the evaluation of fruit maturity. This paper reviews recent progress in the development of IR thermal imaging techniques and suggests possible applications of thermal imaging techniques in agriculture.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.6
• /
• pp.251-256
• /
• 2019

A capsule endoscopy is a medical device that can capture an entire digestive organ from the esophagus to the anus at one time. It produces a vast amount of images consisted of about 8~12 hours in length and more than 50,000 frames on a single examination. However, since the analysis of endoscopic images is performed manually by a medical imaging specialist, the automation requirements of the analysis are increasing to assist diagnosis of the disease in the image. Among them, this study focused on automatic detection of polyp images. A polyp is a protruding lesion that can be found in the gastrointestinal tract. In this paper, we propose a weighted-image generation method to enhance the polyp image learning by multi-scale analysis. It is a way to extract the suspicious region of the polyp through the multi-scale analysis and combine it with the original image to generate a weighted image, that can enhance the polyp image learning. We experimented with SVM and RF which is one of the machine learning methods for 452 pieces of collected data. The F1-score of detecting the polyp with only original images was 89.3%, but when combined with the weighted images generated by the proposed method, the F1-score was improved to about 93.1%.

• Toxicological Research
• /
• v.21 no.2
• /
• pp.135-140
• /
• 2005

Although the pathogenesis of cerebral cavernous malformation (CCM) is unknown, a familial predisposition has been recognized, with up to $55\%$ of patients having an affected relatives. Genetic linkage studies have recently mapped a gene causing CCM to a segment of the long arm of chromosome 7 (7q). We report herein a genetic linkage analysis conducted on a Korean three generation family with CCM. It's first report in Korean family. A Korean family in which one member had undergone surgery for ubtracerebrak hematoma (ICH) and confirmed the CCM, was evaluated. They were examined clinically (n=18) and by magnetic resonance (MR) imaging (n=10). Polymorphic markers (D7S1813, D7S1789) spanning the CCM1 locus on 7q were genotyped by the polymerase chain reaction and analysis of linkage was performed in this family (n=17). Six had multiple lesions on brain MR image, one of them being symptomatic, and five were asymptomatic. Seven remaining members were asymptomatic and refused MR image study. One had died of ICH from presumed CCM. Analysis of the pedigree was consistent with an autosomal dominant pattern of inheritance. All affected patients were linked to CCM1. Linkage to CCM1 can account for inheritance of CCM in this family. They had some striking features with a low clinical penetrance and the presence of multiple lesions. These findings have implications for genetic testing of this disorder and represent an important step toward identification of the gene responsible for the pathogenesis of this disease.

• Korean Journal of Agricultural Science
• /
• v.47 no.1
• /
• pp.119-130
• /
• 2020

With the current rapid growth and increase in the world's population, the demand for nutritious food and fibers and fuel will increase. Therefore, there is a serious need for the use of breeding programs with the full potential to produce high-yielding crops. However, existing breeding techniques are unable to meet the demand criteria even though genotyping techniques have significantly progressed with the discovery of molecular markers and next-generation sequencing tools, and conventional phenotyping techniques lag behind. Well-organized high-throughput plant phenotyping platforms have been established recently and developed in different parts of the world to address this problem. These platforms use several imaging techniques and technologies to acquire data for quantitative studies related to plant growth, yield, and adaptation to various types of abiotic or biotic stresses (drought, nutrient, disease, salinity, etc.). Phenotyping has become an impediment in genomics studies of plant breeding. In recent years, phenomics, an emerging domain that entails characterizing the full set of phenotypes in a given species, has appeared as a novel approach to enhance genomics data in breeding programs. Imaging techniques are of substantial importance in phenomics. In this study, the importance of current imaging technologies and their applications in plant phenotyping are reviewed, and their advantages and limitations in phenomics are highlighted.

• Journal of the Optical Society of Korea
• /
• v.18 no.5
• /
• pp.551-557
• /
• 2014

In this study, we demonstrated multimodal nonlinear optical (NLO) microscopy integrated simultaneously with two-photon excitation fluorescence (TPEF), second-harmonic generation (SHG), and coherent anti-Stokes Raman scattering (CARS) in order to obtain targeted cellular and label-free images in an immunofluorescence assay of the atherosclerotic aorta from apolipoprotein E-deficient mice. The multimodal NLO microscope used two laser systems: picosecond (ps) and femtosecond (fs) pulsed lasers. A pair of ps-pulsed lights served for CARS (817 nm and 1064 nm) and SHG (817 nm) images; light from the fs-pulsed laser with the center wavelength of 720 nm was incident into the sample to obtain autofluorescence and targeted molecular TPEF images for high efficiency of fluorescence intensity without cross-talk. For multicolor-targeted TPEF imaging, we stained smooth-muscle cells and macrophages with fluorescent dyes (Alexa Fluor 350 and Alexa Fluor 594) for an immunofluorescence assay. Each depth-sectioned image consisted of $512{\times}512$ pixels with a field of view of $250{\times}250{\mu}m^2$, a lateral resolution of $0.4{\mu}m$, and an axial resolution of $1.3{\mu}m$. We obtained composite multicolor images with conventional label-free NLO images and targeted TPEF images in atherosclerotic-plaque samples. Multicolor 3-D imaging of atherosclerotic-plaque structural and functional composition will be helpful for understanding the pathogenesis of cardiovascular disease.