• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.187-198
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• 2014

In this paper an efficient image encryption scheme based on cyclic rotations and multiple blockwise diffusions with two chaotic maps is proposed. A Sin map is used to generate round keys for the encryption/decryption process. A Pomeau-Manneville map is used to generate chaotic values for permutation, pixel value rotation and diffusion operations. The encryption scheme is composed of three stages: permutation, pixel value rotation and diffusion. The permutation stage performs four operations on the image: row shuffling, column shuffling, cyclic rotation of all the rows and cyclic rotation of all the columns. This stage reduces the correlation significantly among neighboring pixels. The second stage performs circular rotation of pixel values twice by scanning the image horizontally and vertically. The amount of rotation is based on $M{\times}N$ chaotic values. The last stage performs the diffusion four times by scanning the image in four different ways: block of $8{\times}8$ pixels, block of $16{\times}16$ pixels, principal diagonally, and secondary diagonally. Each of the above four diffusions performs the diffusion in two directions (forwards and backwards) with two previously diffused pixels and two chaotic values. This stage makes the scheme resistant to differential attacks. The security and performance of the proposed method is analyzed systematically by using the key space, entropy, statistical, differential and performance analysis. The experimental results confirm that the proposed method is computationally efficient with high security.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.411-421
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• 2002

In this Paper we propose a new Image analysis algorithm for microarray processing and a method to locate the position of the grid cell using the topology of the grid spots. Microarray is a device which enables a parallel experiment of 10 to 100 thousands of test genes in order to measure the gene expression. Because of the huge data obtained by a experiment automated image analysis is needed. The final output of this microarray experiment is a set of 16-bit gray level image files which consist of grid-structured spots. In this paper we propose one algorithm which located the address of spots (spot indices) using graph structure from image data and a method which determines the precise location and shape of each spot by measuring the inclination of grid structure. Several experiments are given from real data sets.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.3_4
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• pp.138-148
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• 2003

Orthogonal Cross Cylinder mapping and segmentation based modeling methods have been implemented for constructing the image-based navigation system in this paper. The Orthogonal Cross Cylinder (OCC) is the object expressed by the intersection area that occurs when a cylinder is orthogonal with another. OCC mapping method eliminates the singularity effect caused in the environment maps and shows an almost even amount of area for the environment occupied by a single texel. A full-view image from a fixed point-of-view can be obtained with OCC mapping although it becomes difficult to express another image when the point-of-view has been changed. The OCC map is segmented according to the objects that form the environment and the depth value is set by the characteristics of the classified objects for the segmentation based modeling. This method can easily be implemented on an environment map and makes the environment modeling easier through extracting the depth value by the image segmentation. An environment navigation system with a full-view can be developed with these methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.79-86
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• 2003

A new quantization constraint set based on the theory of Projection onto Convex Set(POCS) is proposed to reduce blocking artifact appearing in block-coded images. POCS-based postprocessing for alleviating the blocking artifact consists of iterative projections onto smoothness constraint set and quantization constraint set, respectively. In general, the conventional quantization constraint set has the maximum size of range where original image data can be included, therefore over-blurring of restored image is unavoidable as iteration proceeds. The projection onto the proposed quantization constraint set can reduce blocking artifact as well as maintain the clearness of the decoded image, since it controls adaptively the size of quantization constraint set according to the DCT coefficients. Simulation results using the proposed quantization constraint set as a substitute for conventional quantization constraint set show that the blocking artifact of the decoded image can be reduced by the small number of iterations, and we know that the postprocessed image maintains the distinction of the decoded image.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.128-136
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• 2006

The stippling techniques, which represent objects with numerous points using pen and ink. The previous stippling techniques for Non-Photorsealistc Rendering(NPR) use single-colored points to represent the tone of gray image ur the material of surface. This paper presents a new stippling technique with various colored points based on the analysis of color information. By using the color information of the input image on HSV model, we define the color weight function that allows to determine automatically the number and size of points. The color jittering based on Munsell's color model can generate stippling drawings using various colored points to represent the image. Our color stippling method is expected to be used in many areas such as animation, digital art, video processing and CG tool.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.183-189
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• 2013

This paper proposes a novel lossless image compression scheme composed of direction-adaptive prediction and context-based entropy coding. In the prediction stage, we analyze the directional property with respect to the current coding pixel and select an appropriate prediction pixel. In order to further reduce the prediction error, we propose a prediction error compensation technique based on the context model defined by the activities and directional properties of neighboring pixels. The proposed scheme applies a context-based Golomb-Rice coding as the entropy coding since the coding efficiency can be improved by using the conditional entropy from the viewpoint of the information theory. Experimental results indicate that the proposed lossless image compression scheme outperforms the low complexity and high efficient JPEG-LS in terms of the coding efficiency by 1.3% on average for various test images, specifically for the images with a remarkable direction the proposed scheme shows better results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.9
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• pp.979-986
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• 2010

This paper presents an algorithm for retrieving precise radar cross sections(RCS) of various trihedral corner reflectors (TCR) which are external calibrators of synthetic aperture radar(SAR) systems. The theoretical RCSs of the TCRs are computed based on the physical optics(PO), geometrical optics(GO), and physical theory of diffraction(PTD) techniques; that is, the RCS computation includes the single reflections(PO), double reflections(GO-PO), triple reflections(GO-GO-PO), and edge diffractions(PTD) from the TCR. At first, we acquire an SAR image of the area that five TCRs installed in, and then extract the RCS of the TCRs. The RCSs of the TCRs are extracted accurately from the SAR image by adding up the power spill, which is generated due to the radar IRF(Impulse Response Function), using a square window. We compare the extracted RCSs with the theoretical RCSs and analyze the difference between the theoretical and experimental RCSs of the TCR for various window sizes and various backscattering coefficient levels of the adjacent area. Finally, we propose the minimum size of the integration area and the maximum level of the backscattering coefficients for the adjacent area.

• Journal of Korean Society for Geospatial Information Science
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• v.5 no.1 s.9
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• pp.71-80
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• 1997

This paper suggests a Map Accuracy Standards by analyzing U.S. National Map Accuracy Standards, by considering korean terrain feature and statistical error theory for paper and digital maps on the scale of 1:50,000. Map accuracy standards require horizontal accuracy to be reported as a circular error with 90% confidence level through Linear Error Probable(LEP) theory and Circular Error Probable(CEP) theory. In order to verify the proposed methodology for positioning accuracy testing, several kinds of test point were selected and tested. These test points were extracted at the centers of roads and bridges, the comers of the independent building, the edges of geographical botany, and the tops of mountains. The positioning accuracy assessment was peformed by comparing the positions of test points in digital maps generated three different sources with those acquired by high accurate GPS surveying. The digital maps were produced from aerial photographs and SPOT satellite image using analytical plotter and 1:50,000 paper map.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.9-16
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• 2018

Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.