• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.361-368
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• 2022

This study seeks to determine the research trend of late adolescents by utilizing big data. Also, seek for research trends related to activity participation, treatment, and mediation to provide academic implications. For this process, gathered 1.000 academic papers and used TF-IDF analysis method, and the topic modeling based on co-occurrence word network analysis method LDA (Latent Dirichlet Allocation) to analyze. In conclusion this study conducted analysis of activity participation, treatment, and mediation of late adolescents by TF-IDF analysis method, co-occurrence word network analysis method, and topic modeling analysis based on LDA(Latent Dirichlet Allocation). The results were proposed through visualization, and carries significance as this study analyzed activity, treatment, mediation factors of late adolescents, and provides new analysis methods to figure out the basic materials of activity participation trends, treatment, and mediation of late adolescents.

• Annual Conference of KIPS
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• 2011.04a
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• pp.410-413
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• 2011

본 논문에서는 도로 영상에서 검출된 자동차 영상을 종류별 분류를 위해 효과적인 질감 특징정보 기반의 자동차 종류별 분류 방안을 제안한다. 제안한 연구에서는 운전자의 안전운전지원을 위해 도로상에서 검출된 자동차 영역과 자신의 차량과 거리를 추정하기 위해 검출된 자동차의 종류를 인식할 필요가 있다. 즉, 인식된 자동차의 종류에 따라 차량 간 거리를 추정에 필요한 파라미터로 사용할 수 있기 때문이다. 따라서 본 연구에서는 검출된 자동차 영상들로부터 GLCM(gray-level co-occurrence matrix)의 7가지의 특징정보들을 추출하고 SVM을 사용하여 학습 한 후 자동차의 종류(승용, 화물, 버스)를 분류하는 방법을 제안한다. GLCM은 영상이 가진 질감 정보를 효율적으로 분석함으로써 영역의 밝기 변화 정도, 거침 정도, 픽셀 분포 정도 등을 표현하기 때문에 영상내의 포함된 영역을 분류하는데 효과적이다. 제안한 방법을 실제 자동차 규모별 분류에 적용한 결과 약 83%의 분류 성공률을 제시하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2089-2094
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• 2017

Altmetrics is the measurement index and quantitative data to complement the traditional indicators based on the citation. Altmetrics research has acquired greater importance in the past few years, partly due to the complement to the traditional bibliometrics. This paper aims to reveal the research status and trends in altmetrics research. A total of 187 articles from 2005 to 2017 are obtained and analyzed, illustrating a steady rise (S-mode) in altmetrics research since 2005. Using social network analysis and co-word analysis, the author cooperation network and keyword co-occurrence network are developed. The core scientists and eight international research groups are discovered, reflecting that researchers in this field have a low degree of cooperation. Four topics of altmetrics research are discovered by hierarchical clustering. The results can be useful for the advanced research of altmetrics.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2022.03a
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• pp.37-38
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• 2022

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• Korean Journal of Head & Neck Oncology
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• v.28 no.2
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• pp.135-138
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• 2012

Primary thyroid lymphoma is rare and accounts for less than 5% of all thyroid malignancy. The clinical presentation includes a rapidly enlarging neck mass, associated with dysphagia, dyspnea or hoarseness. The most common histologic type is diffuse large B cell non-Hodgkin's lymphoma. Papillary thyroid carcinoma is the most common type of thyroid malignancy. Co-occurrence of papillary thyroid carcinoma and primary thyroid lymphoma is very rare. Recently, we experienced a case with co-occurrence of papillary thyroid carcinoma and primary thyroid lymphoma in a 79-year-old woman, who had tuberculous lymphadenitis presented as lateral neck mass. We present this case with a review of the literature.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.599-610
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• 2010

Color laser printers are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use their own printing process, each of printed papers from different printers has a little different invisible noise. After the wiener-filter is used to analyze the invisible noises from each printer, we extract some features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, we use total 2,597 images from 7 color laser printers. The results prove that the presented identification method performs well using the noise features of color printed images.

• 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 Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.