Paper Info
Title
Filter learning and evaluation of the computer aided visualization and analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database
Abstract
We present a simple rendering scheme for thoracic CT datasets which yields a color coding based on local differential geometry features rather than Hounsfield densities. The local curvatures are computed on several resolution scales and mapped onto different colors, thereby enhancing nodular and tubular structures. The rendering can be used as a navigation device to quickly access points of possible chest anomalies, in particular lung nodules and lymph nodes. The underlying principle is to use the nodule enhancing overview as a possible alternative to classical CAD approaches by avoiding explicit graphical markers. For performance evaluation we have used the LIDC-IDRI lung nodule data base. Our results indicate that the nodule-enhancing overview correlates well with the projection images produced from the IDRI expert annotations, and that we can use this measure to optimize the combination of differential geometry filters.
Year
DOI
Venue
2010
10.1117/12.843797
Proceedings of SPIE
Keywords
Field
DocType
pulmonary nodules,computer aided detection,computer aided visualization,Hesse rendering,LIDC-IDRI database
CAD,Computer vision,Color-coding,Visualization,Computer-aided,Artificial intelligence,Differential geometry,Rendering (computer graphics),Hounsfield scale,Computing systems,Physics
Conference
Volume
ISSN
Citations 
7624
0277-786X
1
PageRank 
References 
Authors
0.37
3
6
Name
Order
Citations
PageRank
Rafael Wiemker124429.84
ekta dharaiya2102.31
amnon steinberg331.05
thomas buelow453.06
Axel Saalbach54910.28
Vik, T.632.17