• ETRI Journal
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• v.35 no.2
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• pp.207-217
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• 2013

This paper introduces the modular cellular neural network (CNN), which is a new CNN structure constructed from nine one-layer modules with intercellular interactions between different modules. The new network is suitable for implementing many image processing operations. Inputting an image into the modules results in nine outputs. The topographic characteristic of the cell interactions allows the outputs to introduce new properties for image processing tasks. The stability of the system is proven and the performance is evaluated in several image processing applications. Experiment results on texture segmentation show the power of the proposed structure. The performance of the structure in a real edge detection application using the Berkeley dataset BSDS300 is also evaluated.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.1
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• pp.41-46
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• 2005

It is known that mast cells (MCs) are increased in H. pylori-infected gastritis and its increase is mediated by stem cell factor (c-kit ligand). To determine the mechanism of mast cell recruitment and activation by stem cell factor, weinvestigated the expression of stem cell factor receptor (c-kit) in H. pylori-positive and -negative gastric mucosa. Biopsy specimens from 16 H. pylori-negative and 20 positive subjects were examined. H. pylori infection in gastric mucosa was examined by the Warthin-Starry method. MC and c-kit were identified by immunohistochemisty, using a monoclonal antihuman MC tryptase antibody and a polyclonal anti-human c-kit antibody. Densities of MC and c-kit positive cell were measured by a computerized image analysis system. MCs were detected in the lamina propria of both H. pylori-positive and -negative gastric mucosa. Densities of MC and c-kit positive cell were significantly greater in H. pylori-positive than -negative subjects. c-kit was located on the surface of MCs. These results indicate that stem cell factors may be one of the factors involved in mast cell increase and that they activate mast cells by binding with c-kit.

• 대한생식의학회:학술대회논문집
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• 2004.06a
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• pp.76-76
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• 2004

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.549-555
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• 2006

An edge detection is an very important area in image processing and computer vision, General edge detection methods (Robert mask, Sobel mask, Kirsh mask etc) are a good performance to detect step edge in a image but are no good performance to detect step edge in a noses image. We suggested a step edge detection method based on image information measure and neutral network. Using these essential properties of step edges, which are directional and structural and whose gray level distribution in neighborhood, as a input vector to the BP neutral network we get the good result of proposed algorithm. And also we get the satisfactory experimental result using rose image and cell images an experimental and analysing image.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.108-115
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• 2001

In this paper we are attempted to quantitative classification of the three object color regions on a RGB image using of an improved ML(Maximum Likelihood) classification method. A RGB color image consists of three bands i.e., red, green and blue. Therefore it has a 3 dimensional structure in view of the spectral and spatial elements. The 3D structural yokels were projected in RGB cube wherefrom the ML method applied. Between the conventionally and easily usable Box classification and the statistical ML classification based on Bayesian decision theory, we compared and reviewed. Using the ML method we obtained a good segmentation result to classify positive cell nucleus, negative cell Nucleus and background un a immuno-histological breast carcinoma image. Hopefully it is available to diagnosis and prognosis for cancer patients.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.

• Smart Structures and Systems
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• v.8 no.1
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• Radiation Oncology Journal
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• v.35 no.2
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• pp.180-184
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• 2017

Patients with locally advanced lung cancer and very limited pulmonary function (forced expiratory volume in 1 second $[FEV1]{\leq}1L$) have dismal prognosis and undergo palliative treatment or best supportive care. We describe two cases of locally advanced node-positive non-small cell lung cancer (NSCLC) patients with very limited lung function treated with induction chemotherapy and moderate hypofractionated image-guided radiotherapy (Hypo-IGRT). Hypo-IGRT was delivered to a total dose of 45 Gy to the primary tumor and involved lymph nodes. Planning was based on positron emission tomography-computed tomography (PET/CT) and four-dimensional computed tomography (4D-CT). Internal target volume (ITV) was defined as the overlap of gross tumor volume delineated on 10 phases of 4D-CT. ITV to planning target volume margin was 5 mm in all directions. Both patients showed good clinical and radiological response. No relevant toxicity was documented. Hypo-IGRT is feasible treatment option in locally advanced node-positive NSCLC patients with very limited lung function ($FEV1{\leq}1L$).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3911-3925
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• 2018

This paper proposes a novel steganography method to hide secret data during the generation of tangle patterns. Different from the traditional steganography based on modifying natural images, we propose to construct stego images according to the secret messages. We first create a model to group a selected image contour, and define some basic operations to generate various pattern cells. During data hiding, we create a cell library to establish the relationships between cells and secret data. By painting the cell inside the image contour, we create a dense tangle pattern to carry secret data. With the proposed method, a recipient can extract the secret data correctly. Experimental results show that the proposed method has a flexible embedding capacity. The constructed stego tangle image has good visual effects, and is secure against adversaries. Meanwhile, the stego tangle pattern is also robust to JPEG compression.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.9-14
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• 2000

Non-Photorealistic rendering techniques, such as toon rendering, can enhance the quality of hand-drawn cell-animation images greatly with less effort. For this reason, to on rendering is one of the popular techniques used in the cell-animation image production field. The existing toon rendering techniques, however, have not been effective enough for the real-time image processing that the techniques have not been adequate for some processes that needs immediate responses such as virtual-realities, or video games. This paper will suggest the real-time toon rendering to overcome the limits through real-time outline detection and phong shading. In addition, a effective result-image is created as adding a shadow and a execution time remains by real-time through fast shadow generation algorithm.