• 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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1976-1979
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• 2003

본 논문에서는 연속영상에서 잡음과 객체가 잘 분할되지 않는 환경 내에 있는 객체를 자동으로 분할하는 차영상 기반 알고리즘을 제안하였다. 기존의 차영상 기반의 단일 임계간을 이용한 방식에는 잡음에 크게 영향을 받고 배경과 객체가 비슷한 밝기 값을 가지는 경우 잘 추출되지 않는 많은 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하고자 임계값을 설정하는 영역을 축소하여 잡음간섭의 최소화를 구성하였고 축소된 영역 내의 윤곽선정보를 이용하여 배경 밝기 값의 유사함에서 나오는 간섭을 최소화함으로써 정밀한 객체를 추출할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.347-352
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• 2011

Entropy used for measuring the richness in details of the image and absolute mean brightness error(AMBE) providing a change in the image global appearance are two quantitative measures generally used for measuring quality of images. In this paper, we propose an entropy and AMBE-based thresholding method to binalize a given image. The effectiveness of the proposed method is demonstrated by thresholding experiments on nine test images and comparison with other conventional thresholding methods, that is, Otsu method and entropy-based method.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.939-944
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• 2007

Edge is a significant element to represent boundary information between objects in images. There are various edge detection methods, which are based on differential operation, such as Sobel, Prewitt, Roberts, Canny, Laplacian, and etc. However the conventional methods have drawbacks as follow : (i) insensitivity to edges with gentle curve intensity, (ii) detection of double edges for edges with one pixel width. For the detection of edges, not only development of the effective operators but also that of appropriate thresholding methods are necessary. But it is very complicate problem to find an appropriate threshold. In this paper, we propose an edge detection method based on the region separateness between objects to overcome the drawbacks of the conventional methods, and a thresholding method for the proposed edge detection method. We show the effectiveness of the proposed method through experimental results obtained by applying the proposed and the conventional methods to well-known test images.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.273-281
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• 2010

In this paper, we propose an automatic segmentation method of the endo- and epicardial boundary by using ray-casting profile based on intensity distribution and gradient information in CT images. First, endo-cardial boundary points are detected by using adaptive thresholding and seeded region growing. To include papillary muscles inside the boundary, the endo-cardial boundary points are refined by using ray-casting based profile. Second, epi-cardial boundary points which have both a myocardial intensity value and a maximum gradient are detected by using ray-casting based adaptive gradient profile. Finally, to preserve an elliptical or circular shape, the endo- and epi-cardial boundary points are refined by using elliptical interpolation and B-spline curve fitting. Then, curvature-based contour fitting is performed to overcome problems associated with heterogeneity of the myocardium intensity and lack of clear delineation between myocardium and adjacent anatomic structures. To evaluate our method, we performed visual inspection, accuracy and processing time. For accuracy evaluation, average distance difference and overalpping region ratio between automatic segmentation and manual segmentation are calculated. Experimental results show that the average distnace difference was $0.56{\pm}0.24mm$. The overlapping region ratio was $82{\pm}4.2%$ on average. In all experimental datasets, the whole process of our method was finished within 1 second.

• The KIPS Transactions:PartA
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• v.17A no.4
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• pp.189-196
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• 2010

This paper presents an improved marker detection algorithm using hybrid features such as corner, line segment, region, and adaptive threshold values, etc. In usual augmented reality environments, there are often marker occlusion and poor illumination. However, existing ARToolkit fails to recognize the marker in these situations, especially, partial concealment of marker by user, large change of illumination and dim circumstances. In order to solve these problems, the adaptive threshold technique is adopted to extract a marker region and a corner extraction method based on line segments is presented against marker occlusions. In addition, a compensating method, corresponding the marker size and center between registered and extracted one, is proposed to increase the template matching efficiency, because the inside marker size of warped images is slightly distorted due to the movement of corner and warping. Therefore, experimental results showed that the proposed algorithm can robustly detect the marker in severe illumination change and occlusion environment and use similar markers because the matching efficiency was increased almost 30%.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.418-426
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• 2006

This paper proposes a robust and efficient segmentation method for a cardiac ultrasound image taken from a probe inserted into the heart in surgery. The method consists of three steps: initial boundary extraction, whole boundary modification using confidence competition, and local boundary modification using the rolling spoke method. Firstly, the initial boundary is extracted with threshold regions along the global spokes emitted from the center of an ultrasound probe. Secondly, high confidence boundary edges are detected along the global spokes by competing among initial boundary candidate and new candidates achieved by edge and appearance information. finally, the boundary is modified by rolling local spokes along concave regions that are difficult to extract using the global spokes. The proposed method produces promising segmentation results for the ultrasound cardiac images acquired during surgery.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.573-574
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• 2023

화염 영역 검출을 위해 이전 기법에서는 화재 이미지에서 연기제거 및 색상보정을 통해 이미지를 전처리하였다. 그러나 이 기법은 임계값에 영향을 많이 받고, 밝기채널을 이용하여 검출하기 때문에 밤에 일어난 화재 이미지에서는 평균이상의 퍼포먼스를 수행하지만, 주변이 밝은 대낮의 화재 이미지에서는 퍼포먼스가 줄어드는 문제가 있다. 이를 보완하고자 본 논문에서는 CycleGAN을 이용하여 낮 이미지를 밤 이미지로 바꾸어 이미지 전처리를 진행하는 기법을 제안함으로써 화염 감지의 정확도가 개선되었음을 실험을 통해 보여준다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.21-29
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• 2011

At existing skin detection methods using skin color information defined based on the prior knowldege, threshold value to be used at the stage of dividing the backround and the skin region was decided on a subjective point of view through experiments. Also, threshold value was selected in a passive manner according to their background and illumination environments in these existing methods. These existing methods displayed a drawback in that their performance was fully influenced by the threshold value estimated through repetitive experiments. To overcome the drawback of existing methods, this paper propose a skin region detection method using a histogram approximation based on the mean shift algorithm. The proposed method is to divide the background region and the skin region by using the mean shift method at the histogram of the skin-map of the input image generated by the comparison of the similarity with the standard skin color at the CbCr color space and actively finding the maximum value converged by brightness level. Since the histogram has a form of discontinuous function accumulated according to the brightness value of the pixel, it gets approximated as a Gaussian Mixture Model (GMM) using the Bezier Curve method. Thus, the proposed method detects the skin region by using the mean shift method and actively finding the maximum value which eventually becomes the dividing point, not by using the manually selected threshold value unlike other existing methods. This method detects the skin region high performance effectively through experiments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.23-26
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• 2019

본 논문에서는 X-Ray 영상에서 용접한 부분의 기공이나 균열 등의 결함 영역을 추출하는 새로운 방법을 제안한다. 제안된 방법은 세라믹 X-Ray 영상에서 비등방성 확산 필터를 적용하여 영상의 잡음을 제거하고, 수직 및 수평 히스토그램을 각각 적용하여 용접 영역을 추출한 후, 최소 자승법을 적용하여 배경 밝기를 제거하고, 사다리꼴 형태의 Fuzzy Stretching기법을 적용하여 명암 값을 강조하여 결함 영역과 그 외의 영역간의 명암 대비를 강조한다. 그리고 Fuzzy C_Means 알고리즘을 적용하여 결함 영역을 세분화한 후, Fuzzy C_Means을 적용하여 생성된 클러스터들의 중심 명암 값을 이용하여 ${\alpha}_-cut$을 설정한 후에 임계구간을 구하고 영상을 이진화하여 최종적으로 결함 영역을 추출한다. 제안된 방법의 결함 추출 성능을 확인하기 위하여 세라믹 X-Ray 영상을 대상으로 실험한 결과, 기존의 방법보다 결함 영역이 정확히 추출되는 것을 확인할 수 있었다.