Paper Info
Title
Optimal hierarchies for fuzzy object models
Abstract
In radiologic clinical practice, the analysis underlying image examinations are qualitative, descriptive, and to some extent subjective. Quantitative radiology (QR) is valuable in clinical radiology. Computerized automatic anatomy recognition (AAR) is an essential step towards that goal. AAR is a body-wide organ recognition strategy. The AAR framework is based on fuzzy object models (FOMs) wherein the models for the different objects are encoded in a hierarchy. We investigated ways of optimally designing the hierarchy tree while building the models. The hierarchy among the objects is a core concept of AAR. The parent-offspring relationships have two main purposes in this context: (i) to bring into AAR more understanding and knowledge about the form, geography, and relationships among objects, and (ii) to foster guidance to object recognition and object delineation. In this approach, the relationship among objects is represented by a graph, where the vertices are the objects (organs) and the edges connect all pairs of vertices into a complete graph. Each pair of objects is assigned a weight described by the spatial distance between them, their intensity profile differences, and their correlation in size, all estimated over a population. The optimal hierarchy tree is obtained by the shortest-path algorithm as an optimal spanning tree. To evaluate the optimal hierarchies, we have performed some preliminary tests involving the subsequent recognition step. The body region used for initial investigation was the thorax.
Year
DOI
Venue
2013
10.1117/12.2007604
Proceedings of SPIE
Keywords
Field
DocType
Automatic anatomy recognition,hierarchical models,body-wide segmentation,optimal hierarchy,fuzzy models
Computer vision,Complete graph,Population,Vertex (geometry),Fuzzy logic,Correlation,Artificial intelligence,Spanning tree,Hierarchy,Geography,Cognitive neuroscience of visual object recognition
Conference
Volume
ISSN
Citations 
8671
0277-786X
1
PageRank 
References 
Authors
0.36
0
2
Name
Order
Citations
PageRank
Monica M. S. Matsumoto1395.57
Jayaram K. Udupa22481322.29