• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.765-771
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• 2019

Noise in skull X-ray imaging is inevitable, which reduces imaging quality and diagnostic accuracy and increases errors due to the nature of digital imaging devices. Increasing the dose can attenuate noise, but that could lead to big problems with higher exposure dose received by patients. Thus, noise reduction algorithms are actively being studied at low doses to solve dose problems and reduce noise at the same time. Wiener filter and median filter have been widely used, with the disadvantages of poor noise reduction efficiency and loss of much information about imaging boundary. The purpose of this study is to apply total variation (TV) algorithm to skull X-ray imaging that can compensate for the problems of previous noise reduction efficiency to assess quantitatively and compare them. For this study, skull X-ray imaging is obtained using various kVp and mAs using the skull phantom using the X-ray device of Siemens. In addition, contrast to noise ratio (CNR) and coefficient of variation (COV) are compared and measured when noisy image, median filter, Wiener filter and TV algorithm were applied to each phantom imaging. Experiments showed that when TV algorithms were applied, CNR and COV characteristics were excellent under all conditions. In conclusion, we've been able to see if we can use TV algorithm to improve image quality and CNR could be seen to increase due to the decrease in noise as the amount of increased mAs. On the other hand, COV decreased as the amount of increased mAs, and when kVp increased, noise was reduced and the transmittance was increased, so COV was reduced.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.137-140
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• 1996

Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.85-88
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• 2001

Unlike rigid objects or This paper developes the algorithm for segmenting gaseous objects on an image plane. Unlike rigid objects or solid non-rigid objects, gaseous objects vary in density even within single-object regions and the edge intensity differs at different locations. So, an edge detector may detect only strong edges and detected edges may be an incomplete parts of an whole object's boundary. Due to this property of gaseous objects, it is not easy to distinguish the real edges of gaseous objects from the noisy-like edges such as leaves. Our algorithm uses two criteria of edge intensity and edge's line connectivity, then applies fuzzy set so as to obtain the proper threshold of the edge detector

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.180-183
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• 2002

In this paper, we propose new unsharp masking technique based on the multiscale gradient planes. The unsharp masking technique is implemented as a high-pass filter and improves the sharpness of degraded images. However, the conventional unsharp masking enhances the noise component simultaneously. To reduce the noise influence, we introduce the edge information from the difference of the gradient values between two consecutive scales of the multiscale gradient. The multiscale gradient indicates the presence of image edges as the ratio between the gradients between two different scales by its multiscale nature. The noise reduction of the proposed method does not depend on the variance of images and noises. In experiment, we demonstrate enhancement results for blurred noisy images and compare with the conventional cubic unsharp masking technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.381-394
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• 2016

The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.

• Korean Journal of Cognitive Science
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• v.2 no.2
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• pp.341-358
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• 1990

In this paper, A thinning algorithm which can solve a noise problem os proposed. The proposed method is based on the pavlidis thinning algorithm. During a contour tracing period of the given image, the masks of $3{\times}3$ pixels are proposed. They check all possible caseds of the noise conditions. As soon as the contour tracing is finished, the candidates of the noise are automatically deleted. As a result of the implementation of the proposed algorithm, the similar results which is obtained by noise-free image are obtained and they show the simplified structures comparing with the thinning results of the noisy images. Thus, They illustrate that a simple recognition part is needed to identify the objects.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1394-1397
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• 1987

It is difficult to detect edge segments from a noisy image since the image have a noise in piratical applications which utilize some type of visual input capability. Hence, the proposed algorithm consists of the modality tests based on parallel statistical tests without a noise removal preprocessing or postprocessing, and the edge detection technique With one-Pixel edge segments in this paper. The algorithm is very reliable and effective in the case of those situations where the Picture is poor quality and low resolution. And it does'nt require thinning operation and thresholding in hand. Experimental comparision With the more conventional techniques when applied to typical low-quality Pictures confirms good capabilities of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.89-92
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• 1996

In this paper, we developed visual tracking algorithm using double active bar. The active bar model to represent the object can reduce the search space of energy surface and better performance than those of snake model. However, the contour will not find global equilibrium when driving force caused by image may be weak. To overcome this problem. Double active bar is proposed for finding the global minimum point without any dependence on initialization. To achieve the goal, an deformable model with two initial contours in attempted to search for a global minimum within two specific initial contours. This approach improve the performance of finding the contour of target. To evaluate the performance, some experiments are executed. We can achieved the good result for tracking a object on noisy image.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.58-66
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• 1997

This paper proposes a multiresolution surface parameter estimation method for range images. Based on robust estimation of surface parameters, it approximates a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with th eglobal threshold determined by te distribution of the approximation error. The proposed multiresolution surface parameter estimation method is applied to range image reconstruction. Computer simulation results with noisy rnag eimages contaminated by additive gaussian noise and impulse noise show that the proposed multiresolution reconstruction method well preserves step and roof edges compared with the conventional methods. Also the segmentation method based on the estimated surface parameters is shown to be robust to noise.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.17-22
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• 2013

In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.