Active contours driven by difference of Gaussians
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Nature Publishing GroupDate
2017-11-03Citation
10.1038/s41598-017-14502-w
Scientific Reports 7 (2017): 14984
ISSN
2045-2322DOI
10.1038/s41598-017-14502-wFunded by
This research was supported by a predoctoral grant FI-DGR (2014–2017) from the Agency of Management of University and Research Grants (AGAUR), Catalunya, SpainEditor's Version
https://doi.org/10.1038/s41598-017-14502-wSubjects
Active contour; Gaussians; Difference; InformáticaRights
© 2017 The Author(s)Abstract
In this paper, a novel edge-based active contour method is proposed based on the difference of Gaussians (DoG) to segment intensity inhomogeneous images. DoG is known as a feature enhancement tool, which can enhance the edges of an image. However, in the proposed energy functional it is used as an edge-indicator parameter, which acts like a balloon force during the level-set curve evolution process. In the proposed formulation, the internal energy term penalizes the deviation of the level-set function from a signed distance function and external energy term evolves the contour towards the boundaries of the objects. There are three main advantages of the proposed method. First, image difference computed using the DoG function provides the global structure of an image, which helps to segment the image globally that the traditional edge-based methods are unable to do. Second, it has a low time complexity compared to the state-of-the-art active contours developed in the context of intensity inhomogeneity. Third, it is not sensitive to the initial position of contour. Experimental results using both synthetic and real brain magnetic resonance (MR) images show that the proposed method yields better segmentation results compared to the state-of-the-art.
Files in this item
Google Scholar:Akram, Farhan
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García García, Miguel Ángel
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Puig, Domenec
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