Attribute-filtering and knowledge extraction for vessel segmentation

Authors:
Benoît Caldairou;Nicolas Passat;Benoît Naegel
Affiliations:
Université de Strasbourg, LSIIT, UMR CNRS, France and Université de Strasbourg, LINC, UMR CNRS, France;Université de Strasbourg, LSIIT, UMR CNRS, France;Université Nancy 1, LORIA, UMR CNRS, France
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Year:
2010

Citing 12
Cited 1

Attribute openings, thinnings, and granulometries

Computer Vision and Image Understanding
Connected filtering and segmentation using component trees

Computer Vision and Image Understanding
Shape Preserving Filament Enhancement Filtering

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Symbolic Description of 3-D Structures Applied to Cerebral Vessel Tree Obtained from MR Angiography Volume Data

IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Grey-level hit-or-miss transforms-part II: Application to angiographic image processing

Pattern Recognition
Mask-Based Second-Generation Connectivity and Attribute Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Complex Images Based on Component-Trees: Methodological Tools

ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Segmentation of 4D cardiac MRI: Automated method based on spatio-temporal watershed cuts

Image and Vision Computing
Direction-adaptive grey-level morphology. application to 3D vascular brain imaging

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Antiextensive connected operators for image and sequence processing

IEEE Transactions on Image Processing
Building the Component Tree in Quasi-Linear Time

IEEE Transactions on Image Processing

Interactive segmentation based on component-trees

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Attribute-filtering, relying on the notion of component-tree, enables to process grey-level images by taking into account high-level a priori knowledge. Based on these notions, a method is proposed for automatic segmentation of vascular structures from phase-contrast magnetic resonance angiography. Experiments performed on 16 images and validations by comparison to results obtained by two human experts emphasise the relevance of the method.