• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.1
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• pp.128-137
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• 2000

A range of suede-like samples were collected including woven fabric type, nonwoven fabric type, and natural suede(sheep). The surface textures of these suede-like fabrics are rather diverse and different from the plain filament type fabrics since there are a lot of fine surface free fiber ends. Physical and mechanical measurements were carried using the KES equipments. Based on the Kawabata-Niwa translational equation, primary hand values and THV were calculated. Uniaxial tensile tests were performed. Using glossmeter, the reflectance pattern was analyzed at different incidence and receiving angles. In order to capture the surface images of the specimens, a CCD camera and frame grabber connected to a PC were employed. The reflectance uniformity of the images was measured with line-profile analysis and standard deviation values of the profile of the images were calculated. After the multiscale wavelet transfermation, correlation among the transformed image was analyzed at each scale. The reflectance uniformity of the natural suede was better than that of nonwoven type suede, while that of woven type suede was the last among the selected three samples(natural suede, nonwoven type, woven type). The correlation analysis among images has shown the possibility of using the wavelet transformation of the images as one of the measures to detect similarities among the textured specimens.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.326-327
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• 2007

We investigated whether the CT images of hepatic lesions could be analyzed by computer-aided diagnosis (CAD) tool. We retrospectively reanalyzed 14 liver CT images (10 hepatocellular cancers and 4 benign liver lesions; patients who presented with hepatic masses). The hepatic lesions on CT were segmented by rectangular ROI technique and the morphologic features were extracted and quantitated using fractal texture analysis. The contrast enhancement of hepatic lesions was also quantified and added to the differential diagnosis. The best discriminating function combining the textural features and the values of contrast enhancement of the lesions was created using linear discriminant analysis. Textural feature analysis showed moderate accuracy in the differential diagnosis of hepatic lesions, but statistically insignificant. Combining textural analysis and contrast enhancement value resulted in improved diagnostic accuracy, but further studies are needed.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.26-28
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• 1992

Texture analysis, one of the mage processing techniques, using statistical characteristics is applied to the ultrasound images, which are then classified into each types through neural network. This is a method to be used to diagnose ultrasound images automatically and objectively. First tone kinds of texture analysis techniques proposed already are used to classify ultrasound images and compared in terms of classification rate, and then a new technique if proposed which is invariant to multiplicative gain changes and image resolution.

• Journal of Radiation Protection and Research
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• v.48 no.3
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• pp.144-152
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• 2023

Background: Color texture analysis was applied as a tool for quantitative evaluation of radiation-induced skin injuries. Materials and Methods: We prospectively selected 20 breast cancer patients who underwent whole-breast radiotherapy after breast-conserving surgery. Color images of skin surfaces for irradiated breasts were obtained by using a mobile skin analyzer. The first skin measurement was performed before the first fraction of radiotherapy, and the subsequent measurement was conducted approximately 10 days after the completion of the entire series of radiotherapy sessions. For comparison, color images of the skin surface for the unirradiated breasts were measured similarly. For each color image, six co-occurrence matrices (red-green [RG], red-blue [RB], and green-blue [GB] from color channels, red [R], green [G], blue [B] from gray channels) can be generated. Four textural features (contrast, correlation, energy, and homogeneity) were calculated for each co-occurrence matrix. Finally, several statistical analyses were used to investigate the performance of the color textural parameters to objectively evaluate the radiation-induced skin damage. Results and Discussion: For the R channel from the gray channel, the differences in the values between the irradiated and unirradiated skin were larger than those of the G and B channels. In addition, for the RG and RB channels, where R was considered in the color channel, the differences were larger than those in the GB channel. When comparing the relative values between gray and color channels, the 'contrast' values for the RG and RB channels were approximately two times greater than those for the R channel for irradiated skin. In contrast, there were no noticeable differences for unirradiated skin. Conclusion: The utilization of color texture analysis has shown promising results in evaluating the severity of skin damage caused by radiation. All textural parameters of the RG and RB co-occurrence matrices could be potential indicators of the extent of skin damage caused by radiation.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.43-51
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• 2023

Purpose: This study aimed to assess texture analysis(TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis(OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results: The results showed statistically significant differences(P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.

• 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.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.736-746
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• 2016

In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.660-677
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• 2013

The blending of defective texture with the ambience texture results in camouflage. The gray value or color distribution pattern of the camouflaged images fails to reflect considerable deviations between the camouflaged object and the sublimating background demands improved strategies for texture analysis. In this research, we propose the implementation of an initial enhancement of the image that employs line masks, which could result in a better discrimination of the camouflaged portion. Finally, the gray value distribution patterns are analyzed in the enhanced image, to fix the camouflaged portions.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.22-30
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• 2001

In this paper we propose three classification algorithms to classify breast tumors that occur in duct into Benign, DCIS(ductal carcinoma in situ) NOS(invasive ductal carcinoma) The general approach for a creating classifier is composed of 2 steps: feature extraction and classification Above all feature extraction for a good classifier is very significance, because the classification performance depends on the extracted features, Therefore in the feature extraction step, we extracted morphology features describing the size of nuclei and texture features The internal structures of the tumor are reflected from wavelet transformed images with 10$\times$ and 40$\times$ magnification. Pariticulary to find the correlation between correct classification rates and wavelet depths we applied 1, 2, 3 and 4-level wavelet transforms to the images and extracted texture feature from the transformed images The morphology features used are area, perimeter, width of X axis width of Y axis and circularity The texture features used are entropy energy contrast and homogeneity. In the classification step, we created three classifiers from each of extracted features using discriminant analysis The first classifier was made by morphology features. The second and the third classifiers were made by texture features of wavelet transformed images with 10$\times$ and 40$\times$ magnification. Finally we analyzed and compared the correct classification rate of the three classifiers. In this study, we found that the best classifier was made by texture features of 3-level wavelet transformed images.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.