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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우리는 다중해상도 분석 및 위상 초상화 기술을 기반으로 일반화된 그래디언트 벡터 흐름장 기술의 수정을 제안합니다. 원본 이미지는 다중 해상도 분석을 거쳐 일련의 근사 및 세부 이미지를 생성합니다. 근사값은 에지 맵으로 변환된 후 일반화된 그래디언트 벡터 흐름 변환을 받는 그래디언트 필드로 변환됩니다. 이 절차는 노이즈를 제거하고 큰 기울기를 확장합니다. 반복할 때마다 알고리즘은 위상 초상화 분석을 사용하여 필터링되는 새롭고 향상된 벡터 필드를 얻습니다. 위상 초상화는 가능한 경계점과 노이즈를 찾기 위해 가변 크기의 창에 적용됩니다. 이진 규칙을 기반으로 한 이전 단계 초상화 기술과 달리 우리의 방법은 지속적으로 조정 가능한 점수를 생성합니다. 점수는 해당 선형 미분 방정식 시스템의 고유값 함수입니다. 이 방법의 두드러진 특징은 연속성입니다. 점수가 높으면 이미지의 노이즈 부분이 될 가능성이 높지만 점수가 낮으면 객체의 경계가 될 가능성이 높습니다. 점수는 원본 이미지에 적용된 필터에 의해 사용됩니다. 점수가 높은 지점 근처에서는 회색 수준이 부드러워지고 경계 지점에서는 회색 수준이 증가합니다. 다음으로, 새로운 그래디언트 필드가 생성되고 그 결과가 반복적인 그래디언트 벡터 흐름 반복에 통합됩니다. 다중 해상도 분석과 결합된 이 접근 방식은 정확도가 크게 향상되어 강력한 분할이 가능합니다. 합성 및 실제 의료용 초음파 영상에 대한 수치 실험을 통해 제안한 기법이 필터와 다중 해상도를 동일한 방식으로 적용하더라도 기존 그래디언트 벡터 흐름 방법보다 성능이 우수함을 보여줍니다. 마지막으로 제안된 알고리즘은 기존 방법보다 초기 윤곽선이 실제 경계에서 훨씬 더 멀리 떨어져 있음을 보여줍니다.
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Sirikan CHUCHERD, Annupan RODTOOK, Stanislav S. MAKHANOV, "Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2822-2835, October 2010, doi: 10.1587/transinf.E93.D.2822.
Abstract: We propose a modification of the generalized gradient vector flow field techniques based on multiresolution analysis and phase portrait techniques. The original image is subjected to mutliresolutional analysis to create a sequence of approximation and detail images. The approximations are converted into an edge map and subsequently into a gradient field subjected to the generalized gradient vector flow transformation. The procedure removes noise and extends large gradients. At every iteration the algorithm obtains a new, improved vector field being filtered using the phase portrait analysis. The phase portrait is applied to a window with a variable size to find possible boundary points and the noise. As opposed to previous phase portrait techniques based on binary rules our method generates a continuous adjustable score. The score is a function of the eigenvalues of the corresponding linearized system of ordinary differential equations. The salient feature of the method is continuity: when the score is high it is likely to be the noisy part of the image, but when the score is low it is likely to be the boundary of the object. The score is used by a filter applied to the original image. In the neighbourhood of the points with a high score the gray level is smoothed whereas at the boundary points the gray level is increased. Next, a new gradient field is generated and the result is incorporated into the iterative gradient vector flow iterations. This approach combined with multiresolutional analysis leads to robust segmentations with an impressive improvement of the accuracy. Our numerical experiments with synthetic and real medical ultrasound images show that the proposed technique outperforms the conventional gradient vector flow method even when the filters and the multiresolution are applied in the same fashion. Finally, we show that the proposed algorithm allows the initial contour to be much farther from the actual boundary than possible with the conventional methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2822/_p
부
@ARTICLE{e93-d_10_2822,
author={Sirikan CHUCHERD, Annupan RODTOOK, Stanislav S. MAKHANOV, },
journal={IEICE TRANSACTIONS on Information},
title={Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow},
year={2010},
volume={E93-D},
number={10},
pages={2822-2835},
abstract={We propose a modification of the generalized gradient vector flow field techniques based on multiresolution analysis and phase portrait techniques. The original image is subjected to mutliresolutional analysis to create a sequence of approximation and detail images. The approximations are converted into an edge map and subsequently into a gradient field subjected to the generalized gradient vector flow transformation. The procedure removes noise and extends large gradients. At every iteration the algorithm obtains a new, improved vector field being filtered using the phase portrait analysis. The phase portrait is applied to a window with a variable size to find possible boundary points and the noise. As opposed to previous phase portrait techniques based on binary rules our method generates a continuous adjustable score. The score is a function of the eigenvalues of the corresponding linearized system of ordinary differential equations. The salient feature of the method is continuity: when the score is high it is likely to be the noisy part of the image, but when the score is low it is likely to be the boundary of the object. The score is used by a filter applied to the original image. In the neighbourhood of the points with a high score the gray level is smoothed whereas at the boundary points the gray level is increased. Next, a new gradient field is generated and the result is incorporated into the iterative gradient vector flow iterations. This approach combined with multiresolutional analysis leads to robust segmentations with an impressive improvement of the accuracy. Our numerical experiments with synthetic and real medical ultrasound images show that the proposed technique outperforms the conventional gradient vector flow method even when the filters and the multiresolution are applied in the same fashion. Finally, we show that the proposed algorithm allows the initial contour to be much farther from the actual boundary than possible with the conventional methods.},
keywords={},
doi={10.1587/transinf.E93.D.2822},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow
T2 - IEICE TRANSACTIONS on Information
SP - 2822
EP - 2835
AU - Sirikan CHUCHERD
AU - Annupan RODTOOK
AU - Stanislav S. MAKHANOV
PY - 2010
DO - 10.1587/transinf.E93.D.2822
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - We propose a modification of the generalized gradient vector flow field techniques based on multiresolution analysis and phase portrait techniques. The original image is subjected to mutliresolutional analysis to create a sequence of approximation and detail images. The approximations are converted into an edge map and subsequently into a gradient field subjected to the generalized gradient vector flow transformation. The procedure removes noise and extends large gradients. At every iteration the algorithm obtains a new, improved vector field being filtered using the phase portrait analysis. The phase portrait is applied to a window with a variable size to find possible boundary points and the noise. As opposed to previous phase portrait techniques based on binary rules our method generates a continuous adjustable score. The score is a function of the eigenvalues of the corresponding linearized system of ordinary differential equations. The salient feature of the method is continuity: when the score is high it is likely to be the noisy part of the image, but when the score is low it is likely to be the boundary of the object. The score is used by a filter applied to the original image. In the neighbourhood of the points with a high score the gray level is smoothed whereas at the boundary points the gray level is increased. Next, a new gradient field is generated and the result is incorporated into the iterative gradient vector flow iterations. This approach combined with multiresolutional analysis leads to robust segmentations with an impressive improvement of the accuracy. Our numerical experiments with synthetic and real medical ultrasound images show that the proposed technique outperforms the conventional gradient vector flow method even when the filters and the multiresolution are applied in the same fashion. Finally, we show that the proposed algorithm allows the initial contour to be much farther from the actual boundary than possible with the conventional methods.
ER -