• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.95-98
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• 2001

본 논문은 뇌의 축방향(axial sect ion)에 대하여 촬영한 뇌의 자기공명 영상(Magnetic Resonance Imaging)을 대상으로 뇌의 영역만을 분리하기 위한 방법을 제안하고 있다. MR영상은 슬라이스마다 다른 분포값을 가지기 때문에 각 슬라이스 별로 조직의 특성을 파악하여 뇌의 영역을 분리하였다. 히스토그램의 명암값 분포를 분석하여 배경과 뇌를 둘러싸고 있는 외피를 제거하고 라벨링(label1ing) 알고리즘을 적용하여 뇌만 분리 할 수 있도록 하는 마스크 영상을 만들어 이것을 이용하여 원영상으로부터 뇌의 영역만을 분리하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.159-164
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• 2001

본 연구에서 설계하고 구현한 뇌 MR영상 처리기에서는 뇌 MR 영상에서 진단에 필요한 정보들을 자동 추출한다. 의료영상 처리 시에는 수집된 의료영상의 특징을 분석하고 특징들을 분류해야 하며 이를 위해서는 효율적인 특징 추출 알고리즘들 필요하다. 뇌 MR 영상 처리기는 영상의 잡음제거나 영상 강화를 위한 전처리기, 영상의 특징을 추출하기 위한 영역분할기와 전역, 지역 특징 추출기로 구성된다. 뇌 MR 영상 특징 추출을 위한 효율적인 의료영상 처리기의 개발 내용을 기술한다.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.204-207
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• 2001

뇌 MR 영상에서 질환을 자동적으로 진단하고 판별하는 작업은 정상인의 뇌 영상과의 비교를 통해서 가능하다. 정상인과의 뇌 영상 비교를 통하여 보다 정확하게 질병에 대한 근거를 제시할 수가 있기 때문에 이러한 접근 방법들이 여러 의료영상 연구 분야에서 시도되고 있다. 정상인의 뇌 영상과의 비교를 위해서는 우선적으로 해결되어야 하는 것이 현재의 대상 영상이 정상인 뇌의 어느 위치의 영상과 일치하는 지를 판별하는 문제이다. 따라서 본 연구는 이러한 뇌 매핑에 사용될 수 있는 특징들을 추출하기 위한 것으로, 뇌 매핑에 사용되는 특징들을 추출하기 위해서 뇌 MR 영상으로부터 대리영역, 뇌영역, 뇌척수액영역 그리고 눈영역을 분할한 후 이들의 윤곽선, 최소사각형과 각 영역들의 픽셀 정보들을 찾아낸다. 이는 추후 연구할 뇌 매핑을 위한 대분류에 사용될 수 있다.

• The Korean Journal of Nuclear Medicine
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• v.37 no.3
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• pp.162-170
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• 2003

The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.149-154
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• 1999

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray, ultrasonic and CT images. This paper introduces a method that segments and detects lesion for 2 dimensional axial MR brain images automatically. Image segmentation process consists of 2 stages. First stage extracts cerebrum region using thresholding and morphology. In the second stage, white matter, gray matter and cerebrospinal fluid in the cerebrum are extracted using FCM, We could improve processing time as removing uninterested region. Finally symmetry measure and anatomical Knowledge are used to detect lesion.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.517-519
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• 2000

본 논문은 뇌의 수직단면에 대하여 촬영된 자기공명영상에서 뇌 영역을 분리한 후 백질과 회백질 및 뇌척수액을 분리하고 각각의 체적을 산출하기 위한 것이다. 본 연구는 먼저 뇌의 자기공명영상에서 영상의 배경 및 뇌 내부를 둘러싸고 있는 외피 및 지방층으로부터 뇌 영역 전체를 분리하였으며, 부분체적의 문제(partial volume artifact)에 의해 명암값의 번짐 현상을 보이는 뇌의내부 영역에서 각 성분의 부분체적을 산출하여 각 조직을 분리하기 위한 명암 값을 결정한 후 백질과 회백질 및 뇌척수액의 영역을 분리하였다. 본 연구는 뇌의 위축을 보이지 않은 정상인의 자기공명영상을 대상으로 하였으며, 향후, 이러한 연구 결과는 알쯔하이머 병이나 뇌성마비 등과 같은 퇴행성 뇌질환 환자의 뇌 위축정도를 객관적으로 진단하는 방법으로 사용될 수 있도록 하는데 있다.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.45-51
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• 2005

This paper emphasizes on the accomplishment of compensated proton density image and T2 weighted image taken from the shrinkage surface of the Brain. From the images, the Brain's surface shrinkage in the normal image and the surface shrinkage in the abnormal image can be observed. After the separation of white matter, gray matter, and CSF, this algorithm calculates the volume of each of them automatically. Results are subdivided into particular ages and saved in the database to be analyzed and to be processed statistically. Therefore, by using this algorithm the normal and abnormal stages can be detected in the early stages to diagnose. This result easily discernment Alzheimer patient and is useful for Alzheimer diagnostic and early detection.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.705-716
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• 2002

This paper proposes a method which visualizes MRI head data in 3 dimensions with direct volume rendering. Though surface rendering is usually used for MRI data visualization, it has some limits of displaying little speckles because it loses the information of the speckles in the surfaces while acquiring the information. Direct volume rendering has ability of displaying little speckles, but it doesn't treat MRI data because of the data features of MRI. In this paper, we try to visualize MRI head data in 3 dimensions as follows. First, we separate the brain region from the head region of MRI head data, next increase the pixel level of the brain region, then combine the brain region with the increased pixel level and the head region without brain region, last visualizes the combined MRI head data with direct volume rendering. We segment the brain region from head region based on histogram threshold, morphology operations and snakes algorithm. The proposed segmentation method shows 91~95% similarity with a hand segmentation. The method rather clearly visualizes the organs of the head in 3 dimensions.