Paper Info
Title
Fuzzy affinity induced curve evolution
Abstract
In this paper, we present a fuzzy affinity induced curve evolution method for image segmentation without the need for solving PDEs, thereby making level set implementations vastly more efficient. We make use of fuzzy affinity that has been employed in fuzzy connectedness methods as a speed function for curve evolution. The fuzzy affinity consists of two components, namely homogeneity-based affinity and object-feature-based affinity, which take account both boundary gradient and object region information. Ball scale - a local morphometric structure - has been used for image noise suppression. We use a similar strategy for curve evolution as the method in, 1 but simplify the voxel switching mechanism where only one linked list is used to implicitly represent the evolving curve. We have presented several studies to evaluate the performance of the method based on brain MR and lung CT images. These studies demonstrate high accuracy and efficiency of the proposed method.
Year
DOI
Venue
2010
10.1117/12.843793
Proceedings of SPIE
Keywords
Field
DocType
Curve evolution,fuzzy affinity,image segmentation,level set,local scale
Voxel,Homogeneity (statistics),Level set,Image segmentation,Artificial intelligence,Computer vision,Mathematical optimization,Linked list,Fuzzy logic,Algorithm,Image noise,Curve evolution,Physics
Conference
Volume
ISSN
Citations 
7623
0277-786X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Ying Zhuge134120.08
Jayaram K. Udupa22481322.29
Robert W. Miller331.48