• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.95-102
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• 2021

The urinalysis, which is universally accessible to the general public, has disadvantages of being less objective using sight and purchasing a separate portable urinalysis machine. However, due to the high penetration rate and performance improvement of smartphone created by the development of mobile communication technology, research on urinalysis services using smartphone has been conducted. In this paper, a new urinalysis screening application based on smartphone was developed by supplementing the limitations of the previously studied urinalysis services. The key technology of the application is urinalysis recognition algorithm and urinalysis pad color determination algorithm through image-processing and contour detection. In order to confirm the performance of the developed application, urinalysis strip was photographed and analyzed from various backgrounds and angles.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.250-257
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• 2011

This experiment present the automatic detection algorithm of gastric cancer that take second place among all cancers. If an inflammation and a cancer are not examined carefully, early ones have difficulty in being diagnosed as illnesses than advanced ones. For diagnosis of gastric cancer, and progressing cancer in this study, present 4 algorithm. research team extracted an abnormal part in stomach through the endoscope image. At first, decide to use shading technique or not in each endoscope image for study. it make easy distinguish to whether tumor is existing or not by putting shading technique in or eliminate it by the color. Second. By passing image subjoin shading technique to erosion filter, eliminate noise and make give attention to diagnose. Third. Analyzing out a line and fillet graph from image adding surface shade and detect RED value according to degree of symptoms. Fourth. By suggesting this algorithm, that making each patient's endscope image into subdivision graph including RED graph value, afterward revers the color, revealing the position of tumor, this study desire to help to diagnosing gastric, other cancer and inflammation.

• Journal of Korea Multimedia Society
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• v.7 no.2
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• pp.173-186
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• 2004

This paper proposed the content-based retrieval system as a method for performing image retrieval through the effective feature extraction of the object of significant meaning based on the characteristics of man's visual system. To allow the object region of interest to be primarily detected, the region, being comparatively large size, greatly different from the background color and located in the middle of the image, was judged as the major object with a meaning. To get the original features of the image, the cumulative sum of tile declination difference vector the segment of the object contour had and the signature of the bipartite object were extracted and used in the form of being applied to the rotation of the object and the change of the size after partition of the total length of the object contour of the image into the normalized segment. Starting with this form feature, it was possible to make a retrieval robust to any change in translation, rotation and scaling by combining information on the texture sample, color and eccentricity and measuring the degree of similarity. It responded less sensitively to the phenomenon of distortion of the object feature due to the partial change or damage of the region. Also, the method of imposing a different weight of similarity on the image feature based on the relationship of complexity between measured objects using the fractal dimension by the Boxing-Counting Dimension minimized the wrong retrieval and showed more efficient retrieval rate.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.71-78
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• 2021

In order to classify a target image in a cluster of images, the difference in brightness between the object and the background is mainly concerned, which is not easy to classify if the shape of the object is blurred and the sharpness is low. However, there are a few studies attempted to solve these problems, and there is still the problem of not properly distinguishing between wrong pattern and right pattern images when applied to actual data analysis. In this paper, we propose an algorithm that classifies 3D films into sharp and blurry using the width of the pixel values histogram. This algorithm determines the width of the right and wrong images based on the width of the pixel distributions. The larger the width histogram, the sharp the image, while the shorter the width histogram the blurry the image. Experiments show that the proposed algorithm reflects that the characteristics of these histograms allows classification of all wrong images and right images. To determine the reliability and validity of the proposed algorithm, we compare the results with the other obtained from preprocessed 3D films. We then trained the 3D films using few-shot learning algorithm for accurate classification. The experiments verify that the proposed algorithm can perform higher without complicated computations.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• Journal of the Korean Society of Radiology
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• v.8 no.6
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• pp.301-307
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• 2014

In this study, an image acquired by the DSA(Digital Subtraction Angiography) system that is configured to configure the algorithm for high pass filtering algorithm experiments to improve the quality of angiography methods proposed. high pass filter is a high-frequency components pass through the filter, blocking low-frequency components. Part of the boundary line and contour of the organ corresponds to the high-frequency component is a high-frequency component of a medical image. Therefore, the high pass filter is also used for detection of the boundary line, but is also used for the high frequency enhancement. It was able to be analyzed by the proposed algorithm, to improve the quality of the angiography. Found out that the expression of the target site stand out clearly. The quality of the DSA system proposed in the wrong diagnosis software can be used to reduce, it is possible to develop and will further improve the accuracy of the treatment.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.1-8
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• 2012

In this paper, we propose the digital video frame obfuscation method for intellectual property protection using the DC component of the intra frame and the motion vector of the inter frame at digital video encoding. The proposed method considers characteristics of the HVS (human visual system) which is sensitive at the low frequency and the middle frequency. This method makes the signal distorted as operating XOR between authentication signal and the DC coefficient of the intra frame including main information and the sign of the motion vector including edge motion, so that the video is normally displayed only when suitable authentication signal is applied.

• Parasites, Hosts and Diseases
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• v.32 no.1
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• pp.53-56
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• 1994

A case of gastric anisaklasis due to the larva of Pseunotewqnova decipiens was confirmed by gastroendoscoplc examination In April 23, 1991. The patient, residing In Pusan, was a 42-year-old housewife, who complained of severe epigastric pawn and recalled that the symptom suddenly attacked her about 6 hours after eating raw Sebqstes inermis. In the gastroendoscopic examination performed about 9 hours after the onset of the symptom, a long whitish nematode larva penetrating the gastric mucosa in the greater curvature of mid-body was found and removed with a biopsy forcep. The nematode was $29.73{\times}0.94mm$ in size, had an intestinal cecum reaching over mfd-level of the ventnculus and was identified as the 4th stage larva of f decfpiens.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.115-122
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• 2013

Disease diagnostics based on medical imaging is getting popularity day by day. Presence of the atherosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present an automatic image segmentation technique for carotid artery ultrasound images based on active contour approach. In our experimental study, we assume that ultrasound images are properly aligned before applying automatic image segmentation. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Our proposed approach successfully segments the carotid artery images in an automated way to help radiologists to detect plaque easily. Obtained results show the effectiveness of the proposed approach.