Title :
Stratified regularity measures with Jensen-Shannon divergence
Author :
Okada, Kazunori ; Periaswamy, Senthil ; Bi, Jinbo
Author_Institution :
San Francisco State Univ., San Francisco, CA
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 stratificationpsilas 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.
Keywords :
computerised tomography; entropy; feature extraction; learning (artificial intelligence); lung; medical image processing; sensitivity analysis; statistical analysis; CT scan; Jensen-Shannon divergence; data domain stratification; feature extraction method; generalized Shannon entropy; ground-glass lung nodule; machine learning-based computer-aided detection system; medical image analysis problem; stratified regularity measure; Biomedical imaging; Bismuth; Distributed computing; Entropy; Feature extraction; Histograms; Image analysis; Lungs; Pixel; Shape measurement;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4244-2339-2
Electronic_ISBN :
2160-7508
DOI :
10.1109/CVPRW.2008.4563020