• 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 Veterinary Clinics
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• v.24 no.4
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• pp.568-572
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• 2007

This study was carried out to show at the fractal analysis complements the practical disadvantage of gray level histogram which is designed to measure the quantitative classification of echo patterns in ultrasonographic image of parenchymal organs such as spleen and kidney and it is a practical method of measurement for quantitative classification. By using ultrasonographs, kidney and spleen of 21 healthy Beagles were fixed under different gain settings to be scanned for echo patterns and results were analyzed with body gray level histogram and fractal analysis. Then it was compared based on the statistical data obtained. Although there was a proportionate increase in histogram along with gain settings, there were consistencies in the fractal dimension. In terms of quantitative analysis in ultrasonographic images, fractal analysis is concluded to complement the practical disadvantage of gray level histogram.

• 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%.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2975-2980
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• 2015

Edge detection is one of image processing techniques applied for a variety of purposes in a number of areas and it is used as a necessary pretreatment process in most applications. Detect this edge has been conducted in various fields at domestic and international. In the conventional edge detection methods, there are Sobel, Prewitt, Roberts and LoG, etc using a fixed weights mask. Since conventional edge detection methods apply the images to the fixed weights mask, the edge detection characteristics appear somewhat insufficient. Therefore in this study, to complement this, preprocessing using gray-level transformation function and algorithm finding final edge using maximum and minimum value of estimated mask by local mask are proposed. And in order to assess the performance of proposed algorithm, it was compared with a conventional Sobel, Roberts, Prewitt and LoG edge detection methods.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.803-808
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• 2002

This paper describes automatic methods for detection of kidney and kidney tumor on abdominal CT scans. The abdominal CT images were digitalized using a film digitizer and a gray-level threshold method was used to segment the kidney. Based on texture analysis results, which were perform on sample images of kidney tumors, SEED region of kidney tumor was selected as result of homogeneity test. The average and standard deviation, which are representative statistical moments, were used to as an acceptance criteria for homogeneous test. Region growing method was used to segment the kidney tumor from the center pixel of selected SEED region using a gray-level value as an acceptance criteria for homogeneity test. These method were applied to 113 images of 9 cases, which were scanned by GE Hispeed Advantage CT scanner and digitalized by Lumisvs LS-40 film digitizer. The sensitivity was 85% and there was no false-positive results.

• Journal of Digital Contents Society
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• v.4 no.1
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• pp.11-21
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• 2003

In computer vision, the goal of stereopsis is to determine the surface structure of real world form two or more perspective views of scene. It is similar to human visual system. We can avoid obstacles, recognize objects, and manipulate machine using three-dimensional information. Until recently, only gray-level images have been used as input to computation for depth determination, but the availability of color can further enhance the performance of computational stereopsis. There are many models to provide efficient color system. The simplest model, RGB model treats color as if it were composed of separate entities. Each color channel is processed individually by the same stereopsis module as used in the gray-level model. His Model decouples intensity component from color information. So it can deal with color properties without defect intensity information. Opponent color model is based on human visual system. In this model, the red-green-blue colors are combined into three opponent channels before further processing.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.55-61
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• 2007

In this paper, we propose a three-dimensional(3D) active shape models for medical image segmentation. In order to build a 3D shape model, we need to generate a point distribution model(PDM) and select corresponding landmarks in all the training shapes. The manual determination method, two-dimensional(2D) method, and limited 3D method of landmark correspondences are time-consuming, tedious, and error-prone. In this paper, we generate a 3D statistical shape model using the 3D model generation method of a distance transform and a tetrahedron method for landmarking. After generating the 3D model, we extend the shape model training and gray-level model training of 2D active shape models(ASMs) and we use the integrated modeling process with scale and gray-level models for the appearance profile to represent the local structure. Experimental results are comparable to those of region-based, contour-based methods, and 2D ASMs.

• 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.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1247-1254
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• 2003

Currently. the north american symptomatic carotid endarterectomy trial, european carotid surgery trial, and common carotid method are used to measure the carotid stenosis for determining candidate for carotid endarterectomy using the projection angiography from different modalities such as digital subtraction angiography. rotational angiography, computed tomography angiography and magnetic resonance angiography. A new computerized carotid stenosis measuring system was developed using MR angiography axial image to overcome the drawbacks of conventional carotid stenosis measuring methods, to reduce the variability of inter-observer and intra-observer. The gray-level thresholding is one of the most popular and efficient method for image segmentation. We segmented the carotid artery and lumen from three-dimensional time-of-flight MRA axial image using gray-level thresholding technique. Using the measured intima-media thickness value of common carotid artery for each cases, we separated carotid artery wall from the segmented carotid artery region. After that, the regions of segmented carotid without artery wall were divided into region of blood flow and plaque. The calculation of carotid stenosis degree was performed as the following; carotid stenosis grading is(area measure of plaque/area measure of blood flow region and plaque) * 100%.