A statistical analysis of the effects of CT acquisition parameters on low-level features extracted from CT images of the lung

Authors:
Joseph S. Wantroba;Daniela S. Raicu;Jacob D. Furst
Affiliations:
Intelligent Multimedia Processing Laboratory, School of Computing, College of Computing and Digital Media, DePaul University, Chicago, Illinois;Intelligent Multimedia Processing Laboratory, School of Computing, College of Computing and Digital Media, DePaul University, Chicago, Illinois;Intelligent Multimedia Processing Laboratory, School of Computing, College of Computing and Digital Media, DePaul University, Chicago, Illinois
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 2
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Interactions of perceptual and conceptual processing: Expertise in medical image diagnosis

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Abstract

We propose a solution for automatic classification of lung nodules in an environment with heterogeneous Computed Tomography (CT) acquisition parameters. Such a classification system needs to take into account the differences in CT acquisition parameters used when obtaining and processing each medical image. Using Analysis of Variance (ANOVA), our current research proposes to better understand the effects of CT acquisition parameters on predicting various semantic characteristics (such as spiculation, subtlety, and margin) used in the diagnosis interpretation process. All of the parameters were found to affect the low-level image features used in the classification models of these semantic characteristics. When this knowledge is used to normalize those parameters, the final semantic model will become unaffected by the CT acquisition parameters.