Stratified regularity measures with Jensen-Shannon divergence

Kazunori Okada, Senthil Periaswamy, Jinbo Bi
2008 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
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
more » ... itive 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.
doi:10.1109/cvprw.2008.4563020 dblp:conf/cvpr/OkadaPB08 fatcat:ychcywqkz5g3hk7n3quvq6msyi