• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.77-77
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• 2003

CT와 MRI의 단면 영상을 대상으로 영상분할 (Image segmentation)과 Image registration방법을 이용하여 인체 모델을 개발 하고자 한다. 우선 인체의 Head와 Neck부분의 CT와 MR 영상을 얻어 뼈, 근육, 인대, 그리고 그 밖의 장기의 해부학적 영상 특징을 분석하였다. 인체의 Head와 Neck 부분에 대한 CT와 MR 영상에 대해 각 부위별로 ROI(region-of-interrest)를 설정하였고, 각 volxel 마다 3차원 좌표를 계산할 수 있는 소프트웨어를 개발하였다. 특히 각 해부학적 영상에서 부위별로 CT 번호를 분석하고, pulse sequence에 따른 MRI 영상의 부위별 특정을 분석하였다. 이 분석한 자료를 바탕으로 영상 분할을 하였다. 영상 분할전에 각종 잡음(noise) 제거 및 영상 분할을 효과적으로 처리하기 위해 기본적인 영상처리 (filtering)를 구현하였고, 대조도(contrast) 및 밝기(brightness)를 조절할 수 있게 프로그램을 구현하였다. 영상 분할 방법 중 선(line) 및 에지(edge) 의 검출 방법, 문턱치화(threshold) 방법, 영역확대(region growing) 방법으로 영상 분할을 해봄으로써 우리의 인체 모델링 개발에 가장 적합한 영상 분할 알고리듬 방법을 찾도록 시도하였다. 결과적으로 말하면, 한가지 방법의 알고리듬을 쓰는 것보다는 인체의 부위에 따라 두 가지 이상의 알고리듬 방법을 쓰는 것이 원하고자 하는 부위를 영상 분할하는데 더 효과적이다는 것을 알게 되었다. 우리의 연구 과제에서는 영역확대(region growing) 방법과 문턱치화 방법, 모드법(피크니스, 밸리)의 알고리듬을 이용하여 영상 분할을 한 결과 우리가 얻고자 하는 인체 부위별 중 근육과 뼈를 구별하는데는 별 무리가 없었으나, 인대 및 기타 장기를 구별하는데는 어려움을 겪게 되었다. 이후에 좀더 알고리듬을 연구하여 이번 연구에서 구별하기 어려운 장기 부분도 구별 할 수 있도록 노력하겠다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.51-62
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• 2003

A new video segmentation algorithm for segmentation-based video coding is proposed. The method uses a new criterion based on similarities in both motion and brightness. Brightness and motion information are incorporated in a single segmentation procedure. The actual segmentation is accomplished using a region-growing technique based on the watershed algorithm. In addition, a tracking technique is used in subsequent frames to achieve a coherent segmentation through time. Simulation results show that the proposed method is effective in determining object boundaries not easily found using the statistic criterion alone.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1093-1096
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• 1999

In this paper, video image segmentation algorithm based on color histogram and change detector is proposed. Color histograms are calculated from both changed region which is detected in the previous and current frame and unchanged region. With each histogram, modes and valleys are detected. Then, color vectors are calculated by averaging pixels in modes. Markers are extracted by labeling color vectors that represent modes, the watershed algorithm is applied to determine uncertain region. In growing region, the root mean square(RMS) of the distance between average pixel in marker region and adjacent pixel is used as a measure. The proposed algorithm based on color histogram and change detector segments video image fastly and effectively. And simulation results show that the proposed method determines the exact boundary between background and foreground.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.469-480
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• 1997

For moving image coding, the variable size of region coding based on local motion is more efficient than fixed size of region coding. It can be applied well to complex motions and is more stable for wide motions because images are segmented according to local motions. In this paper, new image coding method using the segmentation of motion vectors is proposed. First, motion vector field is smoothed by filtering and segmented by smoothed motion vectors. The region growing method is used for decomposition of regions, and merging of regions is decided by motion vector and prediction errors of the region. Edge of regions is excluded because of the correlation of image, and neighbor motion vectors are used evaluation of current block and construction of region. The results of computer simulation show the proposed method is superior than the existing methods in aspect of coding efficiency.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.19-27
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• 2005

As three-dimensional range scanners make large point clouds a more common initial representation of real world objects, a need arises for algorithms that can efficiently process point sets. In this paper, we present a method for extracting smooth surfaces from dense point clouds. Given an unorganized set of points in space as input, our algorithm first uses principal component analysis to estimate the surface variation at each point. After defining conditions for determining the geometric compatibility of a point and a surface, we examine the points in order of increasing surface variation to find points whose neighborhoods can be closely approximated by a single surface. These neighborhoods become seed regions for region growing. The region growing step clusters points that are geometrically compatible with the approximating surface and refines the surface as the region grows to obtain the best approximation of the largest number of points. When no more points can be added to a region, the algorithm stores the extracted surface. Our algorithm works quickly with little user interaction and requires a fraction of the memory needed for a standard mesh data structure. To demonstrate its usefulness, we show results on large point clouds acquired from real-world objects.

• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.239-246
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• 2000

In this paper. we propose a semi-automatic segmentation algorithm to extract organ in 3D medical data by using a manually segmentation result in a sing1e slice. Generally region glowing based tracking method consists of 3 steps object projection. seed extraction and boundary decision by region growing. But because the boundary between organs in medical data is vague, improper seeds make the boundary dig into the organ or extend to the false region. In the proposed algorithm seeds are carefully extracted to find suitable boundaries between organs after region growing. And the jagged boundary at low gradient region after region growing is corrected by post-processing using Fourier descriptor. Also two-path tracking make it possible to catch up newly appeared areas. The proposed algorithm provides satisfactory results in segmenting 1 mm distance kidneys from X-rav CT body image set of 82 slices.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.29-33
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• 1998

In this paper, we propose a hierarchical segmentation method for tracking a semantic video object using a watershed algorithm based on morphological filtering. In the proposed method, each hierarchy consists of three steps: First, markers are extracted on the simplified current frame. Second, region growing by a modified watershed algorithm is performed for segmentation. Finally, the segmented regions are classified into 3 categories, i.e., inside, outside, and uncertain regions according to region probability values, which are acquired by the probability map calculated from a estimated motion field. Then, for the remaining uncertain regions, the above three steps are repeated at lower hierarchies with less simplified frames until every region is decided to a certain region. The proposed algorithm provides prospective results in video sequences such as Miss America, Clair, and Akiyo.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1449-1455
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• 2012

This paper has developed a system for early diagnosis of senile dementia and mild cognitive impairment (MCI) by developing software to measure the volume of hippocampus. This software consists of two parts; segmentation and analysis. The segmentation part uses ROI and region growing to segment hippocampus region. On the other hand, the analysis part creates a volume rendering of hippocampus. This software is expected contribute in these research fields for dementia diagnosis and its medication planning.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.526-530
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• 2006

논문은 CT영상에서 영역 확장 기법을 이용하여 인간의 장기 중 뇌와 간을 자동으로 추출할 수 있는 방법을 제안한다. 이는 뇌와 간이 CT영상에서 비교적 넓은 영역을 차지하고 있다는 사실에 기인하였으며, CT영상에서 특정 장기 영역을 추출하기 위해서 크게 초기 탐색 영역 결정 단계와 최종 장기 영역 단계로 나누어진다. 초기 탐색 영역은 CT영상 내에서 추출하고자 하는 장기 영역과 관계없는 부분을 제거하고 특정 장기 영역만을 남겨 관심 장기 영역의 검출률을 높이는 작업이다. 본 논문에서는 CT영상에서 비교적 높은 Gray Level을 가지고 있는 뼈영역인 두개골과 척추의 위치를 기반으로 하여 초기 탐색 영역을 결정하는 방법을 사용하였다. 특정 장기 영역의 추출은 ATID(Automatic Threshold Intensity Decision)를 이용한 이진화 단계, 모폴로지의 Opening 기법을 이용한 잡음제거 단계, Region Growing 기법을 이용한 특정 영역 추출 단계를 이용하는 과정을 거친다. 본 논문에서는 Region Growing 기법을 거친 다음 각각의 그룹 중에서 크기가 가장 큰 부분을 최종 특정 장기 영역으로 결정하였다. 본 논문에서 제안한 알고리즘은 국립전남대학교 부속병원에서 수집된 각각 뇌영상 100장과 간영상 100장을 사용하여 실험하였고, 제안된 알고리즘을 통해 관심 장기 영역을 추출했을 경우 약 91%이상의 높은 추출률을 보였다.