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Content Provider | IEEE Xplore Digital Library |
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Author | Kazunori Okada Periaswamy, S. Jinbo Bi |
Copyright Year | 2008 |
Description | Author affiliation: San Francisco State Univ., San Francisco, CA (Kazunori Okada) |
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. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 559447 |
Page Count | 8 |
File Format | |
ISBN | 9781424423392 |
ISSN | 21607508 |
DOI | 10.1109/CVPRW.2008.4563020 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Histograms Image analysis Lungs Shape measurement Bismuth Feature extraction Entropy Distributed computing Pixel Biomedical imaging |
Content Type | Text |
Resource Type | Article |
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