An active contour model for image segmentation based on elastic interaction

Xiang, Yang; Chung, Albert C. S.; Ye, Jian

doi:10.1016/j.jcp.2006.03.026

Cited by 67 publications

(37 citation statements)

References 40 publications

(83 reference statements)

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“…In summary, if two curves interact with each other, they move to minimize the total energy which is a combination of their self-energy and the interaction energy. These formulations are developed based on the elasticity system associated with dislocations in crystals and this concept has been applied to solving image segmentation problem in [25]. As we have stated, non-rigid image registration can be considered as two sets of object boundaries moving and interacting to minimize the total associated energies, we use similar energy as above to achieve the goal of nonrigid image registration.…”

Section: Derivation Of the Proposed Methodsmentioning

confidence: 99%

“…This can greatly reduce the computational complexity in the implementation. The numerical implementation details of this transformation can be found in [25].…”

Section: E Symmetric Diffeomorphic Registration With Dislocation Energymentioning

confidence: 99%

“…As it is shown in Section II-E, the variational problem of the image registration can be solved iteratively by applying the Equation 25. The existence of a solution for v calculated by Equation 25 can be guaranteed by the integrability condition established for the diffeomorphic image registration [28].…”

Section: Appendix Bmentioning

confidence: 99%

“…v is calculated through the updating Equation 25. According to the Picard-Lindelof Theorem, Equation 25 has a classical solution:…”

Section: Appendix Bmentioning

confidence: 99%

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Nonrigid Image Registration With Crystal Dislocation Energy

Luo

Chung

2013

IEEE Trans. on Image Process.

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Abstract-The goal of nonrigid image registration is to find a suitable transformation such that the transformed moving image becomes similar to the reference image. The image registration problem can also be treated as an optimization problem, which tries to minimize an objective energy function that measures the differences between two involved images. In this paper, we consider image matching as the process of aligning object boundaries in two different images. The registration energy function can be defined based on the total energy associated with the object boundaries. The optimal transformation is obtained by finding the equilibrium state when the total energy is minimized, which indicates the object boundaries find their correspondences and stop deforming. We make an analogy between the above processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to object boundaries in images. The newly derived registration energy function takes the global gradient information of the entire image into consideration, and produces an orientation-dependent and long-range interaction between two images to drive the registration process. This property of interaction endows the new registration framework with both fast convergence rate and high registration accuracy. Moreover, the new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the compared methods in terms of both registration accuracy and computation time.

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Section: Derivation Of the Proposed Methodsmentioning

confidence: 99%

“…This can greatly reduce the computational complexity in the implementation. The numerical implementation details of this transformation can be found in [25].…”

Section: E Symmetric Diffeomorphic Registration With Dislocation Energymentioning

confidence: 99%

Section: Appendix Bmentioning

confidence: 99%

“…v is calculated through the updating Equation 25. According to the Picard-Lindelof Theorem, Equation 25 has a classical solution:…”

Section: Appendix Bmentioning

confidence: 99%

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Nonrigid Image Registration With Crystal Dislocation Energy

Luo

Chung

2013

IEEE Trans. on Image Process.

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“…With the evolution of the level set function, the curve is moving implicitly, which promotes the combination with the active contour model. Up to now, in order to provide an effective way, active contour models [3,[5][6][7] based on the theory of curve and Generally, active contour models can be roughly categorized into two different classes: edge-based models [6,[8][9][10][11] and region-based models [7,[12][13][14][15][16][17]. Edge-based models use local image gradient information to attract the active contour toward the object boundaries and stop there.…”

Section: Introductionmentioning

confidence: 99%

An efficient operator splitting method for local region Chan-Vese model

Wang

Huang

Liu

2013

EURASIP J. Adv. Signal Process.

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In this paper, we propose an efficient operator splitting method for local region Chan-Vese (C-V) model for image segmentation. Different from the C-V model, we employ the window function and absorb the local characteristics of the image for improving the C-V model, which we called the local C-V model. The local C-V model can deal with the problem of intensity inhomogeneity which widely exists in the real-world images. By employing a Laplacian operator, we present an operator splitting method to update the level set function. Firstly, we solve the proposed model for evolving the level set function, which drives the active contour to move toward the object boundaries. Secondly, we introduce the Laplacian operator to act on the level set function as a diffusion term, which could efficiently ensure the smoothness and stability and eliminate the complex process of re-initialization. Besides, we increase a new constraint term which avoids updating the level set function seriously. Furthermore, we present an extension for vector-valued images. Experiment results show that our method is competitive with application to synthetic and real-world images.

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Algorithm‐based method for detection of blood vessels in breast MRI for development of computer‐aided diagnosis

Lin

Chen

Nie

et al. 2009

Magnetic Resonance Imaging

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Purpose: To develop a computer-based algorithm for detecting blood vessels that appear in breast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and to evaluate the improvement in reducing the number of vascular pixels that are labeled by computer-aided diagnosis (CAD) systems as being suspicious of malignancy. Materials and Methods:The analysis was performed in 34 cases. The algorithm applied a filter bank based on wavelet transform and the Hessian matrix to detect linear structures as blood vessels on a two-dimensional maximum intensity projection (MIP). The vessels running perpendicular to the MIP plane were then detected based on the connectivity of enhanced pixels above a threshold. The nonvessel enhancements were determined and excluded based on their morphological properties, including those showing scattered small segment enhancements or nodular or planar clusters. The detected vessels were first converted to a vasculature skeleton by thinning and subsequently compared to the vascular track manually drawn by a radiologist. Results:When evaluating the performance of the algorithm in identifying vascular tissue, the correct-detection rate refers to pixels identified by both the algorithm and radiologist, while the incorrect-detection rate refers to pixels identified by only the algorithm, and the missed-detection rate refers to pixels identified only by the radiologist. From 34 analyzed cases the median correct-detection rate was 85.6% (mean 84.9% Ϯ 7.8%), the incorrect-detection rate was 13.1% (mean 15.1% Ϯ 7.8%), and the missed-detection rate was 19.2% (mean 21.3% Ϯ 12.8%). When detected vessels were excluded in the hot-spot color-coding of the CAD system, they could reduce the labeling of vascular vessels in 2.6%-68.6% of hotspot pixels (mean 16.6% Ϯ 15.9%). Conclusion:The computer algorithm-based method can detect most large vessels and provide an effective means in reducing the labeling of vascular pixels as suspicious on a DCE-MRI CAD system. This algorithm may improve the workflow of radiologists using CAD for image display, but will be particularly useful for development of automated CAD that gives diagnostic impression.

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An active contour model for image segmentation based on elastic interaction

Cited by 67 publications

References 40 publications

Nonrigid Image Registration With Crystal Dislocation Energy

Nonrigid Image Registration With Crystal Dislocation Energy

An efficient operator splitting method for local region Chan-Vese model

Algorithm‐based method for detection of blood vessels in breast MRI for development of computer‐aided diagnosis

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