Automated analysis of spatio-temporal features for non-masses

Sebastian Hoffmann; Marc Lobbes; Bernhard Burgeth; Anke Meyer-Bäse

doi:10.1117/12.2019935

29 May 2013 Automated analysis of spatio-temporal features for non-masses

Sebastian Hoffmann, Marc Lobbes, Bernhard Burgeth, Anke Meyer-Bäse

Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 875009 (2013) https://doi.org/10.1117/12.2019935
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Non-mass enhancing lesions represent one of the most challenging types of lesions when it comes to both manual and computer-assisted diagnosis. Compared to the well-characterized mass-enhancing lesions, non-masses have not well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus non-discriminative for malignant and benign non-masses. A valuable feature descriptor should capture the heterogeneity of enhancement as well as the speed of enhancement in the tissue. We apply and evaluate both textural and spatio-temporal descriptors to the pertinent feature extraction of these lesions. An automated computer-aided diagnosis system evaluates the atypical behavior of these lesions, and additionally considers the impact of non-rigid motion compensation on a correct diagnosis.

Citation Download Citation

Sebastian Hoffmann, Marc Lobbes, Bernhard Burgeth, and Anke Meyer-Bäse "Automated analysis of spatio-temporal features for non-masses", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875009 (29 May 2013); https://doi.org/10.1117/12.2019935

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