• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.549-555
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• 2007

In order to study cortical properties in human, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Among many approaches, surface-based method that reconstructs a 3-D model from contour lines on cross-section images is widely used. In general, however, medical brain imaging has some problems such as the complexity of the images, non-linear gain artifacts and so on. Due these limitations, therefore, extracting anatomical structures from imaging data is very a complicated and time-consuming task. In this paper, we present an improved method for extracting contour lines of cortical surface from magnetic resonance images that simplifies procedures of a conventional method. The conventional method obtains contour lines through thinning and chain code process. On the other hand, the proposed method can extract contour lines from comparison between boundary data and labeling image without supplementary processes. The usefulness of the proposed method has been verified using brain image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.95-107
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• 1994

A new efficient contour following algorithm for connected-component labeling processing is proposed. The basic idea of the algorithm is that the total number of downward chain codes is the same as one of upward chain codes along the closed contour. If the chain code direction is upward, then region start mark is assigned at the chain code departure pixel and if the chain code is downward, then region end mark is assigned at the chain code arrival pixel. The proposed algorithm extracts directly the contour information from only the current direction information of chain. This makes the algorithm simple and fast and requires less memory with comparison to the conventional algorithms.The proposed contour following algorithm can be applied to the various kind of image processing such as region filling, restoration and region feature extraction.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.490-492
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• 2002

본 논문은 물 속 유충인 깔따구의 움직임을 관찰한 데이터에 Active Contour Model을 적용하여 깔따구 상태의 특징을 추출하는 방법을 제안한다. 1987년 소개된 Active Contour Model은 주어진 영상에 놓인 커브를 그 커브에 의해 분할된 영상의 에너지 값을 최소화하는 방향으로 진화하게 함으로써 영상 내 객체의 경계를 찾게 하는 영상분할 방법이다. Chan과 Vese에 의해 개선된 Model을 이용하여 다이아지논이 처리되기 전과 후의 깔따구 행동 패턴의 특징을 찾아낸다. 우선 깔따구의 움직임 궤적을 0.25초를 간격으로 관찰하여 구해진 속도벡터의 위상영상을 만든다.그리고 위상영상에 Active Contour를 두어 진화시키면서 시간에 따라 감소하는 에너지 값의 그래프에서 구해진 기울기로 깔따구 행동 패턴의 특징을 추출한다.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.934-936
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• 1995

To get a good result of the feature-based stereo matching, contour of buildings must be extracted exactly. In this paper, an algorithm that extracts contour of flat top buildings exactly is proposed. The Algorithm is composed of three steps. One is to find corner points of 4 types in whole image and another is to extract exact lines between coners by edge following technique, the third is to extract exact contour of buildings using binding structures. We have a good result in extracting contour of buildings.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.357-365
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• 2005

We present a B-spline fitting method based on genetic algorithm for the extraction of object contours from the complex image sequence, where objects with similar shape and intensity are adjacent each other. The proposed algorithm solves common malfitting problem of the existing B-spline fitting methods including snakes. Classical snake algorithms have not been successful in such an image sequence due to the difficulty in initialization and existence of multiple extrema. We propose a B-spline fitting method using a genetic algorithm with a new initial population generation and fitting function, that are designed to take advantage of the contour of the previous slice. The test results show that the proposed method extracts contour of individual object successfully from the complex image sequence. We validate the algorithm by false-positive/negative errors and relative amounts of agreements.

• Journal of Periodontal and Implant Science
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• v.54 no.2
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• pp.108-121
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• 2024

Purpose: The aim of this study was to compare changes in soft and hard tissue and the histologic composition following early implant placement in sites with alveolar ridge preservation or spontaneous healing (SH), as well as implant performance up to 1 year after crown insertion. Methods: Thirty-five patients with either intact buccal bone plates or dehiscence of up to 50% following single-tooth extraction of incisors, canines, or premolars were included in the study. They were randomly assigned to undergo one of three procedures: deproteinized bovine bone mineral with 10% collagen (DBBM-C) covered by a collagen matrix (DBBM-C/CM), DBBM-C alone, or SH. At 8 weeks, implant placement was carried out, and cone-beam computed tomography scans and impressions were obtained for profilometric analysis. Patients were followed up after the final crown insertion and again at 1 year post-procedure. Results: Within the first 8 weeks following tooth extraction, the median height of the buccal soft tissue contour changed by -2.11 mm for the DBBM-C/CM group, -1.62 mm for the DBBM-C group, and -1.93 mm for the SH group. The corresponding height of the buccal mineralized tissue changed by -0.27 mm for the DBBM-C/CM group, -2.73 mm for the DBBM-C group, and -1.48 mm for the SH group. The median contour changes between crown insertion and 1 year were -0.19 mm in the DBBM-C/CM group, -0.09 mm in the DBBM-C group, and -0.29 mm in the SH group. Conclusions: Major vertical and horizontal ridge contour changes occurred, irrespective of the treatment modality, up to 8 weeks following tooth extraction. The DBBM-C/CM preserved more mineralized tissue throughout this period, despite a substantial reduction in the overall contour. All 3 protocols led to stable tissues for up to 1 year.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.33-45
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• 2011

In this paper, we present a method for extracting video objects efficiently by using the modified graph cut algorithm based on contour information. First, we extract objects at the first frame by an automatic object extraction algorithm or the user interaction. To estimate the objects' contours at the current frame, motion information of objects' contour in the previous frame is analyzed. Block-based histogram back-projection is conducted along the estimated contour point. Each color model of objects and background can be generated from back-projection images. The probabilities of links between neighboring pixels are decided by the logarithmic based distance transform map obtained from the estimated contour image. Energy of the graph is defined by predefined color models and logarithmic distance transform map. Finally, the object is extracted by minimizing the energy. Experimental results of various test images show that our algorithm works more accurately than other methods.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.362-371
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• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.231-234
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• 2002

This paper proposes a new approach to extract contour of moving object from compressed video stream. We segment the area of moving object by using motion vector and extract the motion object block from it. And then we describe the connectivity direction of outline moving block, detect the edge related to connectivity direction in the block and finally obtain the contour by connecting the edges. This can divide the moving object only with motion vector and detect the exact contour on the basis of the edge automatically. Also, we can reduce spending time using motion block and remove the noise with directional edge. The experimental results demonstrate the accurate and effective qualify of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1889-1891
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• 2004

The differential white blood cell count plays an important role in the diagnosis of different diseases. It is a tedious task to count these classes of cell manually. An automatic counter using computer vision helps to perform this medical test rapidly and accurately. Most commercial-available automatic white blood cell analysis composed mainly 3 steps including segmentation, feature extraction and classification. In this paper we concentrate on the first step in automatic white-blood-cell analysis by proposing a segmentation scheme that utilizes a benefit of active contour. Specifically, the binary image is obtained by thresolding of the input blood smear image. The initial shape of active is then placed roughly inside the white blood cell and allowed to grow to fit the shape of individual white blood cell. The white blood cell is then separated using the extracted contour. The force that drives the active contour is the combination of gradient vector flow force and balloon force. Our purposed technique can handle very promising to separate the remaining red blood cells.