• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.50-59
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• 2000

In this paper, a new preprocessing method is proposed to speedup the watershed-based image segmentation In the proposed method, the gradient correction values of ramp edges are calculated from the positions and width of the ramp edges using Laplacian operator, and then, unlike the conventional method in which the monoscale or multi scale gradient image is directly used as a reference iImage, the reference image is obtained by adding the threshold value to each position of the ramp edges in the monoscale gradient image And the marker image is reconstructed on the reference image by erosion By preprocessing the image for the watershed transformation in such a manner, we can reduce the oversegmentations far more than those of applying the conventional morphological filter to the simple monoscale or multiscale gradient-based reference image Thus, we can reduce the total image segmentation time by reducing the time of postprocessing of region merging, which consumes most of the processing time In the watershed-based image segmentation, Experimental results indicate that the proposed method can speedup the total image segmentation about twice than those of the conventional methods, without the loss of ramp edges and principal edges around the dense-edge region.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.29-31
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• 2007

In this paper we propose an directional analytic approach in image data space for X-ray image which is detected from the X-ray projection system. Such a radiographic nondestructive testing has long been used for steel-tube inspection and weld monitoring. The welded area and thickness of steel-tube are detected from gradient radiating mechanism based on the evaluation of biased X-ray source position. The welded area is an ellipse type on low contrast X-ray image including noise. Noise originates from most of elements of the system. such as shielding CCD camera, imaging screen, X-ray source, inspected object, electronic circuits and etc.. Projection incorrectness and noise influence on imaging quality is to be represented by vertical pixels' distribution. Space analysis due to vertical direction also shows the segmental possibility between regions by visual edge evaluation.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.1-13
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• 2011

In this paper, we propose a gesture recognition system to recognize motions using the shape information of MHI (Motion History Image). The system acquires MHI to provide information on motions from images with input and extracts the gradient images from such MHI for each X and Y coordinate. It extracts the shape information by applying the shape context to each gradient image and uses the extracted pattern information values as the feature values. It recognizes motions by learning and classifying the obtained feature values with a SVM (Support Vector Machine) classifier. The suggested system is able to recognize the motions for multiple people as well as to recognize the direction of movements by using the shape information of MHI. In addition, it shows a high ratio of recognition with a simple method to extract features.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.203-208
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• 2005

The CGO (Chip on Glass) to be measured a few micro unit is captured by line scan camera for the accuracy of chip inspection. But it is very sensitive to scan speed and lighting conditions. In this paper, we propose the methods to increase the accuracy of faulty detection by image subtraction. Image subtraction is detected faultiness by subtracting the image of a ' perfect ' COG from trot of the sample under tests. For image subtraction to be successful, the two images must be pre챠sely registered The two images is registered by the area segmentation pattern matching, and the result image get by operating the gradient mask image and the image to practice subtraction. A series of experimentation showed that the proposed algorithm shows substantial improvement over the other image subtraction methods.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.95-104
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• 2006

The Gradient Vector Flow (GVF) snake or active contour model offers the best performance for image segmentation. However, there are problems in classical snake models such as the limited capture range and the slow progress into concavity. This paper presents a new method for enhancing the performance of the GVF snake model by extending the external force fields from the neighboring fields and using a modified smoothing method to regularize them. The results on a simulated U-shaped image showed that the proposed method has larger capture range and makes it possible for the contour to progress into concavity more quickly compared with the conventional GVF snake model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.4
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• pp.391-397
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• 2003

This paper is about the digital image decimation algorithm which generates a value of decimated element by an average of a target pixel value and a value of neighbor intelligible element to adaptively reflect the merits of ZOD method and FOD method on the decimated image. First, a target pixel located at the center of sliding window is selected, then the gradient amplitudes of its right neighbor pixel and its lower neighbor pixel are calculated using first order derivative operator respectively. Secondly, each gradient amplitude is divided by the summation result of two gradient amplitudes to generate each intelligible weight. Next, a value of neighbor intelligible element is obtained by adding a value of the right neighbor pixel times its intelligible weight to a value of the lower neighbor pixel times its intelligible weight. The decimated image can be acquired by applying the process repetitively to all pixels in input image which generates the value of decimated element by calculating the average of the target pixel value and the value of neighbor intelligible element.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.439-445
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• 2004

Diffusion tensor image(DTI) exploits the random diffusional motion of water molecules. This method is useful for the characterization of the architecture of tissues. In some tissues, such as muscle or cerebral white matter, cellular arrangement shows a strongly preferred direction of water diffusion, i.e., the diffusion is anisotropic. The degree of anisotropy is often represented using diffusion anisotropy indices (relative anisotropy(RA), fractional anisotropy(FA), volume ratio(VR)). In this study, FA images were obtained using different gradient schemes(N=6, 11, 23, 35. 47). Mean values and the standard deviations of FA were then measured at several anatomic locations for each scheme. The results showed that both mean values and the standard deviations of FA were decreased as the number of gradient directions were increased. Also, the standard error of ADC measurement decreased as the number of diffusion gradient directions increased. In conclusion, different gradient schemes showed a significantly different noise performance and the schem with more gradient directions clearly improved the quality of the FA images. But considering acquisition time of image and standard deviation of FA, 23 gradient directions is clinically optimal.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.87-98
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• 1994

Object-oriented analysis-synthesis coding subdivides each image of a sequence into moving objects and compensates the motion of each object. Thus it can reconstruct real motion better than conventional motion-compensated coding techniques at very-low-bit-rates. It uses a mapping parameter technique for estimating motion information of each object. Since a mapping parameter technique uses gradient operators it is sensitive to redundant details and noise. To accurately determine mapping parameters, we propose a new analysis method using integral projections for estimation of gradient values. Also to reconstruct correctly the local motion the proposed algorithm divides an image into segmented objects each of which having uniform motion information while the conventional one assumes a large object having the same motion information. Computer simulation results with several test sequences show that the proposed image analysis method in object-oriented analysis-synthesis coding shows better performance than the conventional one.