• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.675-683
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• 1987

In general, texture images with regular patterns can be described by using the standard texture model regularity vectors for their shape analysis. Early methods not only take much time but also have computational complexity in obtaining regularity vectors. The proposed some improved preprocessing algorithms for texture analysis. Finally, we showed the utility of the proposed method through texture synthesis by making use of the results of texture analysis.

• Proceedings of the SCSK Conference
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• 2003.09b
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• pp.286-293
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• 2003

In cosmetic formulations, texture plays a key role in ingredient choice and formulation optimization. But texture parameters are often measured by sensorial analysis in the last stages of formulation development. Rheology or texture analysis, used separately, has the benefit of characterizing the behavior of raw materials (e.g. polymers) and controlling and predicting the stability of formulations. SEPPIC has developed rheology and texture analysis protocols to obtain a better understanding of the influence of raw materials on the cosmetic texture of formulations. When used in combination, these two methodologies are complementary and provide useful data regarding the impact of raw material choice on all the development steps: manufacturing procedure, formulation stability, skin feeling.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.121-133
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• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.569-579
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• 2019

Objective: To investigate the usefulness of computed tomography (CT) texture analysis (CTTA) in estimating histologic tumor grade and in predicting disease-free survival (DFS) after surgical resection in patients with hepatocellular carcinoma (HCC). Materials and Methods: Eighty-one patients with a single HCC who had undergone quadriphasic liver CT followed by surgical resection were enrolled. Texture analysis of tumors on preoperative CT images was performed using commercially available software. The mean, mean of positive pixels (MPP), entropy, kurtosis, skewness, and standard deviation (SD) of the pixel distribution histogram were derived with and without filtration. The texture features were then compared between groups classified according to histologic grade. Kaplan-Meier and Cox proportional hazards analyses were performed to determine the relationship between texture features and DFS. Results: SD and MPP quantified from fine to coarse textures on arterial-phase CT images showed significant positive associations with the histologic grade of HCC (p < 0.05). Kaplan-Meier analysis identified most CT texture features across the different filters from fine to coarse texture scales as significant univariate markers of DFS. Cox proportional hazards analysis identified skewness on arterial-phase images (fine texture scale, spatial scaling factor [SSF] 2.0, p < 0.001; medium texture scale, SSF 3.0, p < 0.001), tumor size (p = 0.001), microscopic vascular invasion (p = 0.034), rim arterial enhancement (p = 0.024), and peritumoral parenchymal enhancement (p = 0.010) as independent predictors of DFS. Conclusion: CTTA was demonstrated to provide texture features significantly correlated with higher tumor grade as well as predictive markers of DFS after surgical resection of HCCs in addition to other valuable imaging and clinico-pathologic parameters.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1635-1648
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• 2008

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). Finally, to demonstrate the suitability of 3D texture features for grading, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%. As a comparative study, we also performed a stepwise feature selection. Using the 4 optimized features, we could obtain more improved accuracy of 84.32%. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.17-21
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• 2016

Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

• Applied Microscopy
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• v.32 no.3
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• pp.185-193
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• 2002

The works of texture electron diffraction patterns for crystal structure analysis are written in Russian or introduced briefly in books written in English, which makes it difficult to be understood. In addition to working out the equations, vector theory corrects some errors included in the established formulas for texture electron diffraction patterns.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1428-1435
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• 1989

In this paper we propose a new algorithm which extracts spatial arrangement information of texture elements in structural textures. In the proposed algorithm, by the use of projection information in several directions obtained from the texture image we can get two directions which determine the texture structure and the parallelogram grid which isolates texture elements. The isolated texture elements are analyzed and used to synthesize texture images. Computer simulation shows that the proposed method can extract proper spatial structure of the texture element even when the texture image is highly corrupted by additive noise.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.201-205
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• 2020

The structural texture is defined as a form which a texel is regularly repeated in the texture. Structural texture analysis/recognition has various industrial applications, such as automatic inspection of textiles, automatic testing of metal surfaces, and automatic analysis of micro images. In this paper, we propose a Convolution Neural Network (CNN) based system for structural texture analysis. The proposed method learns texles, which are components of textures to be classified. Then, this trained CNN recognizes a structural texture using a partial image obtained from input texture. The experiment shows the superiority of the proposed system.