Paper Info
Title
Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition
Abstract
Computerized automatic anatomy recognition (AAR) is an essential step for implementing body-wide quantitative radiology (QR). Our strategy to automatically identify and delineate various organs in a given body region is based on fuzzy models and an organ hierarchy. In previous years, the basic algorithms of our AAR approach - model building, recognition, and delineation - and their evaluation were presented. In the present paper, we propose to replace the single fuzzy model built for each organ by a set of fuzzy models built for the same organ. Based on a dataset composed of CT images of the Thorax region of 50 subjects, our experiments indicate that recognition performance improves when using multiple models instead of a single model for each organ. It is interesting to point out that the improvement is not uniform for all organs, leading us to conclude that some organs will benefit from the multiple model approach more than others.
Year
DOI
Venue
2014
10.1117/12.2044297
Proceedings of SPIE
Keywords
Field
DocType
Shape Modeling,Fuzzy Sets,Object Recognition,Segmentation,Fuzzy Connectedness,Fuzzy Models,Multiple Models
Anatomy,Computer science,Model building,Fuzzy set,Artificial intelligence,Hierarchy,Computer vision,Segmentation,Fuzzy logic,Object model,Machine learning,Multiple Models,Cognitive neuroscience of visual object recognition
Conference
Volume
ISSN
Citations 
9034
0277-786X
1
PageRank 
References 
Authors
0.35
3
3
Name
Order
Citations
PageRank
leticia rittner110.35
Jayaram K. Udupa22481322.29
D. A. Torigian38121.68