The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 T1 강조 영상, T2 강조 영상, PD 영상의 상호보완적 활용을 통해 MR 뇌 영상의 자동화된 분할 알고리즘을 제안하였다. 제안하는 분할 알고리즘은 3단계로 구성된다. 첫 번째 단계는 세 개의 입력 이미지 위에 대뇌 마스크를 배치하여 대뇌 이미지를 추출하는 것입니다. 두 번째 단계에서는 3차원 클러스터 중에서 대뇌 내부 조직을 대표하는 뛰어난 클러스터를 선택합니다. 3차원 클러스터는 3개의 최적 축척 이미지를 사용하여 형성된 2차원 공간에서 3차원 히스토그램의 조밀하게 분포된 부분을 교차하여 결정됩니다. 최적의 스케일 이미지는 각 2차원 히스토그램과 검색 그래프 구조에 스케일 공간 필터링을 적용한 결과입니다. 결과적으로 최적의 스케일 이미지는 2차원 히스토그램에서 조밀하게 분포된 픽셀 부분의 모양을 정확하게 묘사할 수 있습니다. 마지막 단계에서는 우수 클러스터 중심값을 초기 중심값으로 사용하는 FCM(Fuzzy c-means) 알고리즘을 통해 대뇌 영상을 분할한다. 클러스터 중심 값을 정확하게 계산하는 제안된 분할 알고리즘의 기능은 사용된 초기 중심 값에 의해 과도하게 제한되는 FCM 알고리즘의 현재 한계를 보상합니다. 또한 다중 스펙트럼 분석을 포함하는 제안된 알고리즘은 단일 스펙트럼 분석보다 더 나은 분할 결과를 얻을 수 있습니다.
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부
Ock-Kyung YOON, Dong-Min KWAK, Bum-Soo KIM, Dong-Whee KIM, Kil-Houm PARK, "Automated Segmentation of MR Brain Images Using 3-Dimensional Clustering" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 4, pp. 773-781, April 2002, doi: .
Abstract: This paper proposed an automated segmentation algorithm for MR brain images through the complementary use of T1-weighted, T2-weighted, and PD images. The proposed segmentation algorithm is composed of 3 steps. The first step involves the extraction of cerebrum images by placing a cerebrum mask over the three input images. In the second step, outstanding clusters that represent the inner tissues of the cerebrum are chosen from among the 3-dimensional (3D) clusters. The 3D clusters are determined by intersecting densely distributed parts of a 2D histogram in 3D space formed using three optimal scale images. The optimal scale image results from applying scale-space filtering to each 2D histogram and a searching graph structure. As a result, the optimal scale image can accurately describe the shape of the densely distributed pixel parts in the 2D histogram. In the final step, the cerebrum images are segmented by the FCM (Fuzzy c-means) algorithm using the outstanding cluster center value as the initial center value. The ability of the proposed segmentation algorithm to calculate the cluster center value accurately then compensates for the current limitation of the FCM algorithm, which is unduly restricted by the initial center value used. In addition, the proposed algorithm, which includes a multi spectral analysis, can achieve better segmentation results than a single spectral analysis.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_4_773/_p
부
@ARTICLE{e85-d_4_773,
author={Ock-Kyung YOON, Dong-Min KWAK, Bum-Soo KIM, Dong-Whee KIM, Kil-Houm PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Automated Segmentation of MR Brain Images Using 3-Dimensional Clustering},
year={2002},
volume={E85-D},
number={4},
pages={773-781},
abstract={This paper proposed an automated segmentation algorithm for MR brain images through the complementary use of T1-weighted, T2-weighted, and PD images. The proposed segmentation algorithm is composed of 3 steps. The first step involves the extraction of cerebrum images by placing a cerebrum mask over the three input images. In the second step, outstanding clusters that represent the inner tissues of the cerebrum are chosen from among the 3-dimensional (3D) clusters. The 3D clusters are determined by intersecting densely distributed parts of a 2D histogram in 3D space formed using three optimal scale images. The optimal scale image results from applying scale-space filtering to each 2D histogram and a searching graph structure. As a result, the optimal scale image can accurately describe the shape of the densely distributed pixel parts in the 2D histogram. In the final step, the cerebrum images are segmented by the FCM (Fuzzy c-means) algorithm using the outstanding cluster center value as the initial center value. The ability of the proposed segmentation algorithm to calculate the cluster center value accurately then compensates for the current limitation of the FCM algorithm, which is unduly restricted by the initial center value used. In addition, the proposed algorithm, which includes a multi spectral analysis, can achieve better segmentation results than a single spectral analysis.},
keywords={},
doi={},
ISSN={},
month={April},}
부
TY - JOUR
TI - Automated Segmentation of MR Brain Images Using 3-Dimensional Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 773
EP - 781
AU - Ock-Kyung YOON
AU - Dong-Min KWAK
AU - Bum-Soo KIM
AU - Dong-Whee KIM
AU - Kil-Houm PARK
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2002
AB - This paper proposed an automated segmentation algorithm for MR brain images through the complementary use of T1-weighted, T2-weighted, and PD images. The proposed segmentation algorithm is composed of 3 steps. The first step involves the extraction of cerebrum images by placing a cerebrum mask over the three input images. In the second step, outstanding clusters that represent the inner tissues of the cerebrum are chosen from among the 3-dimensional (3D) clusters. The 3D clusters are determined by intersecting densely distributed parts of a 2D histogram in 3D space formed using three optimal scale images. The optimal scale image results from applying scale-space filtering to each 2D histogram and a searching graph structure. As a result, the optimal scale image can accurately describe the shape of the densely distributed pixel parts in the 2D histogram. In the final step, the cerebrum images are segmented by the FCM (Fuzzy c-means) algorithm using the outstanding cluster center value as the initial center value. The ability of the proposed segmentation algorithm to calculate the cluster center value accurately then compensates for the current limitation of the FCM algorithm, which is unduly restricted by the initial center value used. In addition, the proposed algorithm, which includes a multi spectral analysis, can achieve better segmentation results than a single spectral analysis.
ER -