Application of the mutual information criterion for feature selection in computer-aided diagnosis

The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature...

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Bibliographic Details
Published in:Medical physics (Lancaster) 2001-12, Vol.28 (12), p.2394-2402
Main Authors: Tourassi, Georgia D., Frederick, Erik D., Markey, Mia K., Floyd, Carey E.
Format: Article
Language:English
Subjects:
acute pulmonary embolism
Computational complexity
Computer aided diagnosis
computer‐assisted diagnosis
Data analysis
Diagnosis, Computer-Assisted - methods
feature extraction
feature selection
genetic algorithms
Genomic techniques
Humans
Image analysis
image texture
Information and communication theory
lung
Lungs
medical image processing
Models, Statistical
mutual information
Normal Distribution
Physicists
Software
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Summary:The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. Consequently, MI can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables. This study was based on a database of statistical texture features extracted from perfusion lung scans. The ultimate goal was to select the optimal subset of features for the computer-aided diagnosis of acute pulmonary embolism (PE). Initially, the study addressed issues regarding the approximation of MI in a limited dataset as it is often the case in CAD applications. The MI selected features were compared to those features selected using stepwise linear discriminant analysis and genetic algorithms for the same PE database. Linear and nonlinear decision models were implemented to merge the selected features into a final diagnosis. Results showed that the MI is an effective feature selection criterion for nonlinear CAD models overcoming some of the well-known limitations and computational complexities of other popular feature selection techniques in the field.
ISSN:0094-2405
2473-4209
DOI:10.1118/1.1418724