Fast geodesic active contours

Goldenberg, Roman; Kimmel, Ron; Rivlin, Ehud; Rudzsky, Michael

doi:10.1109/83.951533

Cited by 284 publications

(84 citation statements)

References 37 publications

(49 reference statements)

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“…The semiautomatic interactive segmentation exploits a geodesic active contour model, according to image gray‐scale and spatial information, to delineate the liver boundary 18 , 19 . The liver segmentation was also performed by the surgeon who conducted the manual liver volumetry.…”

Section: Methodsmentioning

confidence: 99%

“…This semiautomatic interactive segmentation exploits a geodesic active contour model, according to image gray‐scale and spatial information, to delineate the liver boundary (18) . The semiautomatic liver segmentation method may be summarized as follows:

(a) The image is preprocessed with a Gaussian smoothing filter, and the gradient image is computed.

(b) The initial contour for level set algorithm is extracted.

…”

Section: Appendicesmentioning

confidence: 99%

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Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Cai

Fan

et al. 2016

J Applied Clin Med Phys

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This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM‐based, and automatic PA‐based liver volumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use.PACS number(s): 87.55.‐x

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Section: Methodsmentioning

confidence: 99%

(a) The image is preprocessed with a Gaussian smoothing filter, and the gradient image is computed.

(b) The initial contour for level set algorithm is extracted.

…”

Section: Appendicesmentioning

confidence: 99%

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Cai

Fan

et al. 2016

J Applied Clin Med Phys

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“…In the first group, also called snakes, the contour generally converges towards edges in the image [3,4,5]. The second group generally has an energy function based on region properties, such as the intensity variance of the enclosed segment [6,7]. These level set approaches have gained a lot of interest since they have some benefits over snakes.…”

Section: Introductionmentioning

confidence: 99%

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Vylder

Ochoa

Philips

et al. 2011

Lecture Notes in Computer Science

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Abstract. Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset.

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“…However, the speed of the standard level set method is slow, which is a main obstacle for real applications [19]. Recently, there is a trend to develop efficient optimizing methods for variational image processing approaches [20][21][22][23]. Among various methods, the class of finding global minimum of variational models has drawn much attention [22,23].…”

Section: Introductionmentioning

confidence: 99%

Variational Sar Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method

Feng¹,

Cao

Pi³

2012

PIER B

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Abstract-This paper present a fast algorithm for synthetic aperture radar (SAR) image segmentation based on the augmented Lagrangian method (ALM). The proposed approach considers the segmentation of SAR images as an energy minimization problem in a variational framework. The energy functional is formulated based on the statistical characteristic of SAR images. The total variation regularization is used to impose the smoothness constraint of the segmentation result. To solve the optimization problem efficiently, the energy functional is firstly modified to be convex and differentiable by using convex relaxing and variable splitting techniques, and then the constrained optimization problem is converted to an unconstrained one by using the ALM. Finally the energy is minimized with an iterative minimization algorithm. The effectiveness of the proposed algorithm is validated by experiments on both synthetic and real SAR images.

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Fast geodesic active contours

Cited by 284 publications

References 37 publications

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Variational Sar Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method

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