• Journal of the Korean Society for Precision Engineering
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• v.27 no.6
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• pp.64-74
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• 2010

This paper proposes a new TR-based baseline-free SHM technique in which the time-reversal (TR) property of the guided Lamb waves is utilized. The new TR-based SHM technique has two distinct features when compared with the other TR-based SHM techniques: (1) The backward TR process commonly conducted by the measurement is replaced by the computation-based process; (2) In place of the comparison method, the TOF information of the damage signal extracted from the reconstructed signal is used for the damage diagnosis in conjunction with the imaging method which enables us to represent the damage as an image. The proposed TR-based SHM technique is then validated through the damage diagnosis experiment for an aluminum plate with a damage at different locations.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.133-143
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• 2023

In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.309-315
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• 2009

Image reconstruction of Inverse Fourier Transform after Frequency Domain Data is filtered applies to Image signal acquired from MR. There are various kinds of image processing techniques; image preprocessing, image reconstruction, image compression, image restoration image mixture, noise and artifact elimination, and image quality improvement. In this paper, optimum filter applicable to diagnosis in clinic by comparing and analyzing the characteristics of the filter will be explained. Fermi-Dirac filter will improve the image quality better than the previous MR image.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.123-131
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• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.399-402
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• 2005

For efficient and accurate diagnosis of ultrasound images, the time gain compensation (TGC) and dynamic range (DR) control of the ultrasound echo signal are important. TGC is for compensating the attenuation of the ultrasound echo signal along the depth, and DR is used to control the image contrast. In this paper, we propose an algorithm for finding the optimized values of TGC and DR automatically. For TGC, the degree of compensation is determined along the depth based on the effective attenuation estimation of ultrasound signal. For DR optimization, we introduce a novel cost function on the basis of the characteristics of ultrasound image, which provides the minimum value at the optimal DR. Experiments have been performed by applying the proposed algorithm to a real US imaging system. The results show that the algorithm automatically can determine the values of TGC and DR in realtime so that the subjective quality of the corresponding US image may be good enough for diagnosis.

• Biomedical Science Letters
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• v.10 no.2
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• pp.115-120
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• 2004

To investigate the correlation between the magnetic resonance imaging (MRI) of cerebral fat embolism that is induced by injecting oleic acid into 10 cats, and a pathologic diagnosis. Using a microcatheter, 30 ${mu}ell$ of oleic acid was injected into the internal carotid artery of 10 cats. MR T2-weighted image (T2WI), diffusion-weighted image (DWI) and Gadolinium-enhanced T1-weighted image (Gd-enhanced T1WI) were obtained after 30 minutes and 2 hours of embolization. After 30 minutes of the embolization, lesions of very high signal intensity were detected by T2WI in 6 cats, and of slightly high signal intensity in 2 cats; in the remaining 2 cats, signal intensity was normal. DWI showed lesions of very high intensity in 9 cats and of slightly high intensity in one cat. According to the findings of light microscopic examination, infarcted lesions mainly involved the gray matter, but also some white matter. A magnetic resonance imaging diagnosis for cerebral fat embolism that was induced by oleic acid through the internal carotid artery in cats showed high signal intensity on the T2WI and the DWI within an initial 2 hours, and with a well enhancement on the Gd-enhanced T1WI. Considering cellular edema, cerebrovascular injury and extracellular space widening, we assumed pathologically that cytotoxic and vasogenic edema exists at the same time.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.715-723
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• 2009

The use of medical imaging in hospitals is being gradually increased as it is of utmost importance in treatment and diagnosis of patients. With the drastic increase of the usage of medical imaging in hospitals per day necessitates more speedy and accurate systems for precise diagnosis and the treatment. Hence the modality and development of network infrastructure are also need to be improved day by day and this trend may be continued. Thus there is a great need improvement of PACS concerned. In this paper, by using the computational grid technology, we design a medical image storage system that improve the compression speed, and implement a prototype as a part of PACS. We also demonstrate the performance improvement from experimental results of the prototype.

• Journal of the Korean Society of Safety
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• v.26 no.2
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• pp.26-31
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• 2011

In this paper, the safety diagnosis using thermal image analysis is described for power equipments. The conventional three-phase comparison method has only provided the results of thermal comparison for the equipments. The proposed method defines the conditions of poor connection by visual checks, and supports the criteria with each thermal rise step. As a result, the thermal difference from $5^{\circ}C$ to $10^{\circ}C$ meant the warning state. In addition, the thermal difference more than $10^{\circ}C$ meant that the connection status was unbalanced. In this case, the countermeasure might be the internal load distribution. If the thermal difference more than $20^{\circ}C$ is observed, it means a hot spot at the poor connection. If the hot spot is observed all over the surface, its cause was the unbalanced load, which made the conductive parts discolored and raised the possibility of oxidization or $Cu_2O$ generation. This diagnostic technology employing thermal image analysis method can be directly applied in the field and ensures the safety of equipments.