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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Kazunori Okada , San Francisco State University, CA USA
Senthil Periaswamy , Siemens Medical Solutions, Malvern, PA USA
Jinbo Bi , Siemens Medical Solutions, Malvern, PA USA
ABSTRACT
This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. Jensen-Shannon divergence is used to compute a set-similarity of intensity distributions derived from stratified data. We prove that derived regularity measures form a continuum as a function of the stratification’s granularity and also upper-bounded by the Shannon entropy. This enables to interpret it as a generalized Shannon entropy with an intuitive spatial parameterization. This measure is applied as a novel feature extraction method for a real-world medical image analysis problem. The proposed measure is employed to describe ground-glass lung nodules whose shape and intensity distribution tend to be more irregular than typical lung nodules. Derived descriptors are then incorporated into a machine learning-based computer-aided detection system. Our ROC experiment resulted in 83% success rate with 5 false positives per patient, demonstrating an advantage of our approach toward solving this clinically significant problem.
CITATION
Kazunori Okada, Senthil Periaswamy, Jinbo Bi, "Stratified regularity measures with Jensen-Shannon divergence", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563020
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