Fourier-based geometric shape prior for snakes

Charmi, Mohamed Ali; Derrode, Stéphane; Ghorbel, Faouzi

doi:10.1016/j.patrec.2008.01.011

Cited by 39 publications

(40 citation statements)

References 21 publications

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“…In Figure (c), the yellow contour converged correctly by increasing the contour weighting parameters, i.e. values 5 3 and 2 3 respectively for parameters α and β. However due to these strong smoothness constraints, the green and blue contours lose the real leaf borders near the tip of the leaves.…”

Section: Resultsmentioning

confidence: 86%

“…More complex internal energy functions, e.g. incorporating prior shape knowledge, have also been reported in literature [10,11]. The external energy is derived from the image, so that the snake will be attracted to features of interest.…”

Section: Snakesmentioning

confidence: 99%

“…If the objects of interest has a specific shape or if they have more pronounced local features, e.g. jags, then the internal probability could be formulated using a more complex shape model, such as the models proposed in [11,10].…”

Section: Internal Probabilitymentioning

confidence: 99%

“…Two main groups can be distinguished in the active contour framework: one group representing the active contour explicitly as a parameterized curve and a second group which represents the contour implicitly using level sets. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 86%

Section: Snakesmentioning

confidence: 99%

Section: Internal Probabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Vylder

Ochoa

Philips

et al. 2011

Lecture Notes in Computer Science

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“…The sectored snakes [20] deform the contour under constraints derived from a priori knowledge of the object shape. Fourier snakes [21] evolve to a prescribed shape defined by a template. Region-based image features are combined with the edge-based features incorporated in the external forces [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow

Chucherd

Rodtook

Makhanov

2010

IEICE Trans. Inf. & Syst.

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SUMMARYWe 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. key words: phase portrait analysis, multiresolution analysis, medical image processing

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Segmentation of brain MRI using active contour model

Rabeh

Benzarti

Amiri

2017

Int J Imaging Syst Tech

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Alzheimer disease is a neurodegenerative disorder that impairs memory, cognitive function, and gradually leads to dementia, physical deterioration, loss of independence, and death of the affected individual. In this context, segmentation of medical images is a very important technique in the field of image analysis and Computer-Assisted Diagnosis. In this article, we introduce a new automatic method of brain images' segmentation based on the Active Contour (AC) model to extract the Hippocampus and the Corpus Callosum (CC). Our contribution is to combine the geometric method with the statistical method of the AC. We used the Caselle Level Set and added a learning phase to build an average shape and to make the initialization task automatic. For the step of contour evolution, we used the principle of Level set and we added to it the a priori knowledge. Experimental results are very promising.

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Fourier-based geometric shape prior for snakes

Cited by 39 publications

References 21 publications

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow

Segmentation of brain MRI using active contour model

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