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Segmentation with Active Contours
- Source :
- Image Processing On Line, Image Processing On Line, IPOL-Image Processing on Line, 2021, 11, pp.120-141. ⟨10.5201/ipol.2021.298⟩, Image Processing On Line, 2021, 11, pp.120-141. ⟨10.5201/ipol.2021.298⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; Active contours (also known as snakes) have shown their ability to introduce regularity on image segmentation. In contrast with level-set approaches, the active contours techniques based on a contour parameterization are able to maintain the initial topology of the area of interest. For this reason, it has been used in recent medical research for diaphragm segmentation. Most of the on-line codes for 2D/3D segmentation, as well as built-in Matlab toolboxes are based on level-set methods. Moreover, in the literature, the implementation details of active contours methods with meshes in three dimensions are tight, making tedious any reproduction of these techniques. In this paper, we give some details of the implementation of active contours in 2D/3D with meshes, especially about the choice of the use of a 2D/3D mesh and its refinement. We also explore the choice of the parameters with a quantitative study of their influence on the segmentation results. The 3D segmentation method has been applied to CT scan images of the lungs.
- Subjects :
- medicine.diagnostic_test
business.industry
Computer science
Active contours
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
medical imaging
Computed tomography
Area of interest
Image segmentation
Initial topology
3. Good health
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Signal Processing
Medical imaging
medicine
Computer vision
Segmentation
Polygon mesh
Artificial intelligence
MATLAB
business
computer
image segmentation
Software
computer.programming_language
Subjects
Details
- Language :
- English
- ISSN :
- 21051232
- Database :
- OpenAIRE
- Journal :
- Image Processing On Line, Image Processing On Line, IPOL-Image Processing on Line, 2021, 11, pp.120-141. ⟨10.5201/ipol.2021.298⟩, Image Processing On Line, 2021, 11, pp.120-141. ⟨10.5201/ipol.2021.298⟩
- Accession number :
- edsair.doi.dedup.....8456490907141a0353a29d076634f173
- Full Text :
- https://doi.org/10.5201/ipol.2021.298⟩