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On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.

Abdelsamea MM, Gnecco G, Gaber MM, Elyan E - Comput Intell Neurosci (2015)

Bottom Line: Moreover, they can handle also topological changes of the contours.SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models.In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.

ABSTRACT
Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

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Related in: MedlinePlus

The architecture of the SISOM-based ACM proposed in [38].
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Related In: Results  -  Collection


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fig3: The architecture of the SISOM-based ACM proposed in [38].

Mentions: Figure 3 illustrates the evolution procedure of the SISOM-based ACM. On the left-side of the figure, the neurons of the map are represented by gray circles, while the black circle represents the winner neuron associated with the current input to the map (in this case, the gray circle on the right-hand side of the figure, which is connected by the gray segments to all the neurons of the map). On the right-hand side, instead, the positions of the white circles represent the initial prototypes of the neurons, whereas the positions of the black circles represent their final values, at the end of learning. The evolution of the contour is controlled by the learning algorithm above, which guides the evolution of the prototypes of the neurons of the SOM (hence, of the active contour) using the points of the edge map as inputs to the SOM learning algorithm. As a result, the final contour is represented by a series of prototypes of neurons located near the actual boundary of the object to be segmented.


On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.

Abdelsamea MM, Gnecco G, Gaber MM, Elyan E - Comput Intell Neurosci (2015)

The architecture of the SISOM-based ACM proposed in [38].
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417572&req=5

fig3: The architecture of the SISOM-based ACM proposed in [38].
Mentions: Figure 3 illustrates the evolution procedure of the SISOM-based ACM. On the left-side of the figure, the neurons of the map are represented by gray circles, while the black circle represents the winner neuron associated with the current input to the map (in this case, the gray circle on the right-hand side of the figure, which is connected by the gray segments to all the neurons of the map). On the right-hand side, instead, the positions of the white circles represent the initial prototypes of the neurons, whereas the positions of the black circles represent their final values, at the end of learning. The evolution of the contour is controlled by the learning algorithm above, which guides the evolution of the prototypes of the neurons of the SOM (hence, of the active contour) using the points of the edge map as inputs to the SOM learning algorithm. As a result, the final contour is represented by a series of prototypes of neurons located near the actual boundary of the object to be segmented.

Bottom Line: Moreover, they can handle also topological changes of the contours.SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models.In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.

ABSTRACT
Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

Show MeSH
Related in: MedlinePlus