• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.833-840
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• 1987

A new Block Truncation Coding(BTC) technique reducing the bit-plane and using differential pulse code modulation (DPCM) is proposed and compared with the conventional BTC methods. A new technique decides whether the subblock can be approximated to be uniform or not. If the subblock can be approximated to be uniform(merge mode), we transmit only the gray-level informantion. It not (split mode), we transmity both the bit-plane and the gray-level information. DPCM method is proposed to the encoding of gray-level information when the subblock can be approximated to be uniform. Also modified quantization method is presented to the encoding of gray-level information when the subblock is not uniform. This technique shows the results of coding 256 level images at the average data rate of about 0.75 bits/pel.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.11
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• pp.30-41
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• 2010

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.407-410
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• 1999

Purpose; To find out the best imaging parameters for the diagnosis of disc in MRI imaging. Materials and methods; The diagnostic quality of the disc among the Tl, PD and T2 images of same patients02 joints, 223 images) was compared by visual(I-IV grades) and gray level measurement (pre- and infra-discal area) method. Results; PD images showed best results with 43.7％ of the images belonging to grade III (good) and with statistically significant higher difference of the gray levels at pre- and infra-discal areas. But there were no grade N (excellent) images. Conclusions; PD images are best method among Tl. PD and T2 images in diagnosing the disc but since there were no excellent images further imaging parameters should be studied for better images.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.133-138
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• 2015

This paper presents a performance improvement of gray level co-occurrence matrix(GLCM) based on the nonuniform quantization, which is generally used to analyze the texture of images. The nonuniform quantization is given by Lloyd algorithm of recursive technique by minimizing the mean square error. The nonlinear intensity levels by performing nonuniformly the quantization of image have been used to decrease the dimension of GLCM, that is applied to reduce the computation loads as a results of generating the GLCM and calculating the texture parameters by using GLCM. The proposed method has been applied to 30 images of $120{\times}120$ pixels with 256-gray level for analyzing the texture by calculating the 6 parameters, such as angular second moment, contrast, variance, entropy, correlation, inverse difference moment. The experimental results show that the proposed method has a superior computation time and memory to the conventional 256-level GLCM method without performing the quantization. Especially, 16-gray level by using the nonuniform quantization has the superior performance for analyzing textures to another levels of 48, 32, 12, and 8 levels.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.417-422
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• 1999

Purpose : The aim of this study was to assess the validity of standard deviation of gray scale histogram in digital subtraction radiography as a test parameter for superimposition error. Materials and Methods : Twenty periapical radiographs were used as baseline images and they were copied to exclude the influence of exposure geomety and contrast differences. These subsequent images were linearly displaced by 0.1-0.5 mm in the x-. y- and xy-directions, rotated by 0.5-3° and distorted by angular contraction of 1-5° in x- and y-axis before subtraction. The standard deviations of gray levels in the subtraction images were obtained and paired t-tests were performed. Pearson correlation coefficients(r) were calculated between the standard deviations and the superimposition errors. Results : Linear displacement showed high correlation coefficients of 0.997, 0.997 and 0.995 in x-. y- and xy-axis respectively. Statistically significant different standard deviation existed among all linearly displaced groups(p<0.05). Distortion showed relatively low correlation coefficients of 0.982 and 0.959 in x- and y-axis. The standard deviations between the two distortion groups were statistically significant different(p<0.05). Conclusion : Standard deviation of gray level distribution in digital subtraction images is satisfactory but not perfect similarity measure to assess the superimposition errors.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.53-62
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• 2012

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.629-634
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• 2011

Empty space in the parking lot of the parking space to analyze the effective use of technology and transportation may be useful in the jungle, However, conventional analytical methods impractical parking space or need a fast processing speed. In this paper, real-time parking, so parking monitoring methods for detection is proposed. Gray-level images using the proposed method to determine whether the parking and the parking space was used to analyze. To verify the performance of the proposed method in an outdoor parking lot 129 video capture and analysis of experimental results in 98.5% of the parking space, parking space, the success of the proposed method was proved to be effective in the analysis.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.