• Journal of fish pathology
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• v.24 no.3
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• pp.247-254
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• 2011

The purpose of this study was to obtain reference data of parameters for hematological health diagnosis in cultured freshwater fish and also evaluate application of automatic dry-type chemistry analyzer (FUJI DRI-CHEM 3000) used to those blood tests. A blood profile of total 200 fish for rainbow trout (Onchorhynchus mykiss), israel carp (Cyprinus carpio), tilapia (Oreochromis niloticus) and eel (Anguilla japonica) cultured in Inland Fisheries Research Institute of NFRDI was determined by hemoglobin (Hb) and plasma chemistry tests: total protein (TP), albumin (ALB), alkaline phosphatase (ALP), blood urea nitrogen (BUN), lactate dehydrogenase (LDH), triglyceride (TG), total cholesterol (TCHO), creatinine (CRE), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glucose (GLU). The values of ALT, TG, LDH, ALB, TCHO, AST and ALP were outside from the minimum and/or maximum of the established detectable range of the analyzer. ALT and TG were not detectable in the range of 67%~61.5%. LDH, ALB and TCHO were not detectable in the range of 36~17%. AST and ALP were not detectable in the range of 5.5~0.5%. However, the values of BUN, CRE, GLU, Hb and TP were below the detectable limits of the analyzer.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.49-55
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• 2017

Computed tomographic angiography (CTA) is widely used in the diagnosis and treatment of coronary artery disease because it shows not only the whole anatomical structure of the cardiovascular three-dimensionally but also provides information on the lesion and type of plaque. However, due to the large size of the image, there is a limitation in manually extracting coronary arteries, and related researches are performed to automatically extract coronary arteries accurately. As the coronary artery originate from the ascending aorta, the ascending aorta and ostium should be detected to extract the coronary tree accurately. In this paper, we propose an automatic segmentation for the ostium as a starting structure of coronary artery in CTA. First, the region of the ascending aorta is initially detected by using Hough circle transform based on the relative position and size of the ascending aorta. Second, the volume of interest is defined to reduce the search range based on the initial area. Third, the refined ascending aorta is segmented by using a two-dimensional geodesic active contour. Finally, the two ostia are detected within the region of the refined ascending aorta. For the evaluation of our method, we measured the Euclidean distance between the result and the ground truths annotated manually by medical experts in 20 CTA images. The experimental results showed that the ostia were accurately detected.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.51-57
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• 2012

Hepatic vessel tree is the key structure for hepatic disease diagnosis and liver surgery planning. Especially, it is used to evaluate the donors' and recipients' liver for the LDLT(Living Donors Liver Transplantation) and estimate the volumes of left and right hepatic lobes for securing their life in the LDLT. In this study, we propose a method to apply canny edge detection that is not affected by noise to the liver images for automatic segmentation of hepatic vessels tree in contrast abdominal MDCT image. Using histograms and average pixel values of the various liver CT images, optimized parameters of the Canny algorithm are determined. It is more time-efficient to use the common parameters than to change parameters manually according to CT images. Candidates of hepatic vessels are extracted by threshold filtering around the detected the vessel edge. Finally, using a system which detects the true-negatives and the false-positives in horizontal and vertical direction, the true-negatives are added in candidate of hepatic vessels and the false-positives are removed. As a result of the process, the various hepatic vessel trees of patients are accurately reconstructed in 3D.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.585-592
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• 2005

One of the most important things in the researches on diseases is to attach more importance to prevention of a disease and preservation of health than to treatment of a disease, also to foods rather than to medicines. In this context, the most significant concern in examining a patient is to find the presence of disease, and, if any, to diaguose the type of disease, after which a pharmacotherapy is followed. In this paper, various diagnosis methods of Oriental medicines are discussed. And ocular inspection, the most important method among the 4 disease diagnoses of Oriental medicines, is studied. Observing a person's shape and color has been the major method for ocular inspection, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for ocular inspection. As the first stage, we applied the signal processing techniques to automatic feature extraction of faces for ocular inspection. Firstly, facial regions are extracted from the point of frontal view, which was followed by extraction of their features. The experiment applied to 20 persons showed that frontal face regions are perfectly extracted, as well as their features, such as eyes, eyebrows, noses and mouths. Future work will seek to address the issues of morphological operation for a few unfinished extraction results, such as combined hair and eyebrows.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.553-560
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• 2005

Generally, it is known that the spleen accompanied by liver cirrhosis is hypertrophied or enlarged. We examined the wave pattern of the spleen by using abdominal CT images of a patient with liver cirrhosis, and found that they are different from those of a person with a normal liver In the abdominal CT image of the patient with liver cirrhosis, there is a deep wave part on the left side of the spleen. In the case of the normal liver, there are waves on the left side, but they aren't deep. Therefore, the total area of waving parts of the spleen with liver cirrhosis is found to be greater than that of the spleen with the normal liver. Moreover, when examining circularity by abstracting the waves of the spleen from the image iO liver cirrhosis, we found they are more circular than those of the spleen accompanied by a normal liver. This paper suggests an automatic method to diagnose splenic enlargement by using the wave pattern of the spleen in abdominal CT images on the basis of the two principles. It tells us that we can judge if the abdomen has a focal splenic enlargement automatically, without the manual test of the size of spleen, only with the shape of spleen.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.532-541
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• 2005

We examined the wave pattern of the spleen by using abdominal CT images of a patient with liver cirrhosis, and found that they are different from those of a person with a normal liver. In the abdominal CT image of the patient with liver cirrhosis, there is a deep wave part on the left side of the spleen. In the case of the normal liver, there are waves on the left side, but they aren't deep. Therefore, the total area of waving parts of the spleen with liver cirrhosis is found to be greater than that of the spleen with the normal liver. Moreover, when examining circularity by abstracting the waves of the spleen from the image with liver cirrhosis, we found they are more circular than those of the spleen accompanied by a normal liver. This paper suggests an automatic method to diagnose liver cirrhosis by using the wave pattern of the spleen in abdominal CT images on the basis of the two principles. It tells us that we can judge if the liver has liver cirrhosis automatically, without the manual test of the ratio of caudate lobe to right lobe, only with the spleen.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.69-77
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• 2005

The electronic nose (e-nose) has been used in food industry and quality controls in plastic packaging. Recently it finds its applications in medical diagnosis, specifically on detection of diabetes, pulmonary or gastrointestinal problem, or infections by examining odors in the breath or tissues with its odor characterizing ability. Moreover, the use of portable e-nose enables the on-site measurements and analysis of vapors without extra gas-sampling units. This is expected to widen the application of the e-nose in various fields including point-of-care-test or e-health. In this study, a PDA-based portable e-nose was developed using micro-machined gas sensor array and miniaturized electronic interfaces. The rich capacities of the PDA in its computing power and various interfaces are expected to provide the rapid and application specific development of the diagnostic devices, and easy connection to other facilities through information technology (IT) infra. For performance verification of the developed portable e-nose system, Six different vapors were measured using the system. Seven different carbon-black polymer composites were used for the sensor array. The results showed the reproducibility of the measured data and the distinguishable patterns between the vapor species. Additionally, the application of two typical pattern recognition algorithms verified the possibility of the automatic vapor recognition from the portable measurements. These validated the portable e-nose based on PDA developed in this study.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.15-27
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• 2007

In Korea, hepatoma is the thirdly frequent cause of death from cancer occupying 17.2% among the whole deaths from cancer and the rate of death from hepatoma comes to about 21's persons per one-hundred thousand ones. This paper proposes an automatic method for the extraction of areas being suspicious as hepatoma from a CT scan and evaluates the availability as an auxiliary tool for the diagnosis of hepatoma. For detecting tumors in the internal of the liver from CT scans, first, an area of the liver is extracted from about $45{\sim}50's$ CT scans obtained by scanning in 2.5-mm intervals starting from the lower part of the chest. In the extraction of an area of the liver, after unconcerned areas outside of the ribs being removed, areas of the internal organs are separated and enlarged by using intensity information of the CT scan. The area of the liver is extracted among separated areas by using information on position and morphology of the liver. Since hepatoma is a hypervascular turner, the area corresponding to hepatoma appears more brightly than the surroundings in contrast-enhancement CT scans, and when hepatoma shows expansile growth, the area has a spherical shape. So, for the extraction of areas of hepatoma, areas being brighter than the surroundings and globe-shaped are selected as candidate ones in an area of the liver, and then, areas appearing at the same position in successive CT scans among the candidates are discriminated as hepatoma. For the performance evaluation of the proposed method, experiment results obtained by applying the proposed method to CT scans were compared with the diagnoses by radiologists. The evaluation results showed that all areas of the liver and liver tumors were extracted exactly and the proposed method has a high availability as an auxiliary diagnosis tools for the discrimination of liver tumors.