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Oblique Random Forests for 3-D Vessel Detection Using Steerable Filters and Orthogonal Subspace Filtering
- Source :
- Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging ISBN: 9783642366192, MCV, MICCAI Workshop on Medical Computer Vision (MCV), MICCAI Workshop on Medical Computer Vision (MCV), Oct 2012, Nice, France. pp.142-154, ⟨10.1007/978-3-642-36620-8_15⟩
- Publication Year :
- 2013
- Publisher :
- Springer Berlin Heidelberg, 2013.
-
Abstract
- International audience; We propose a machine learning-based framework using oblique random forests for 3-D vessel segmentation. Two different kinds of features are compared. One is based on orthogonal subspace filtering where we learn 3-D eigenspace filters from local image patches that return task optimal feature responses. The other uses a specific set of steerable filters that show, qualitatively,similarities to the learned eigenspace filters, but also allow for explicit parametrization of scale and orientation that we formally generalize to the 3-D spatial context. In this way, steerable filters allow to efficiently compute oriented features along arbitrary directions in 3-D. The segmentation performance is evaluated on four 3-D imaging datasets of the murine visual cortex at a spatial resolution of 0.7μm. Our experiments show that the learning-based approach is able to significantly improve the segmentation compared to conventional Hessian-based methods. Features computed based on steerable filters prove to be superior to eigenfilter-based features for the considered datasets. We further demonstrate that random forests using oblique split directions outperform decision tree ensembles with univariate orthogonal splits
- Subjects :
- Hessian matrix
business.industry
Orientation (computer vision)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Decision tree
[SCCO.COMP]Cognitive science/Computer science
Oblique case
Pattern recognition
02 engineering and technology
Random forest
03 medical and health sciences
symbols.namesake
0302 clinical medicine
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
symbols
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
Image resolution
030217 neurology & neurosurgery
Mathematics
Subjects
Details
- ISBN :
- 978-3-642-36619-2
- ISBNs :
- 9783642366192
- Database :
- OpenAIRE
- Journal :
- Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging ISBN: 9783642366192, MCV, MICCAI Workshop on Medical Computer Vision (MCV), MICCAI Workshop on Medical Computer Vision (MCV), Oct 2012, Nice, France. pp.142-154, ⟨10.1007/978-3-642-36620-8_15⟩
- Accession number :
- edsair.doi.dedup.....b6cf45bf21218e3801e42631e035033f