Paper Info
Title
Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration
Abstract
This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.
Year
DOI
Venue
2014
10.1117/12.2044315
Proceedings of SPIE
Keywords
Field
DocType
Shape modeling,fuzzy models,object recognition,fuzzy connectedness,segmentation,registration
Affine transformation,Anatomy,Fuzzy set,Artificial intelligence,Binary number,Computer vision,Fuzzy model,Pattern recognition,Segmentation,Fuzzy logic,Real image,Cognitive neuroscience of visual object recognition,Physics
Conference
Volume
ISSN
Citations 
9036
0277-786X
2
PageRank 
References 
Authors
0.41
9
5
Name
Order
Citations
PageRank
kaioqiong sun120.41
Jayaram K. Udupa22481322.29
Dewey Odhner333943.49
Yubing Tong49322.73
D. A. Torigian58121.68