Cluster-based vector-attribute filtering for CT and MRI enhancement
- Resource Type
- Conference
- Authors
- Kiwanuka, Fred N.; Wilkinson, Michael H. F.
- Source
- Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) Pattern Recognition (ICPR), 2012 21st International Conference on. :3112-3115 Nov, 2012
- Subject
- Computing and Processing
Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Vectors
Shape
Biomedical imaging
Noise
Computed tomography
Foot
Manuals
- Language
- ISSN
- 1051-4651
Morphological attribute filters modify images based on properties or attributes of connected components. Usually, attribute filtering is based on a scalar property which has relatively little discriminating power. Vector-attribute filtering allow better description of characteristic features for 2D images. In this paper, we extend vector attribute filtering by incorporating unsupervised pattern recognition, where connected components are clustered based on the similarity of feature vectors. We show that the performance of these new filters is better than those of scalar attribute filters in enhancement of objects in medical volumes.