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Healthcare Informatics, Imaging and Systems Biology, IEEE International Conference on (2012)
La Jolla, CA, USA USA
Sept. 27, 2012 to Sept. 28, 2012
ISBN: 978-1-4673-4803-4
pp: 86-95
This paper proposes the method called Feature Selection based on the Compactness Measure from Scatterplots (FSCoMS) to select the best features extracted from medical images aiming at improving the effectiveness of Content-Based Image Retrieval. This feature selection algorithm consists in a compactness analysis of scatterplots to find the most relevant features providing high separability abilities. A high relevance value of a scatterplot means better predictability among of classes based on two features. We take advantage of this information to generate a ranking for features usefulness. We compared our method to two well-known feature selection methods using three real medical datasets. All of them were compared regarding the dimensionality of the final feature vector and the retrieval effectiveness measured by the precision and recall graphs. The performed experiments show that our method not only obtained the highest retrieval performance but also achieved the smallest number of demanded features (dimensionality) than the other methods analyzed.
Information Gain, Feature selection, Scatterplot, Medical Images, CFS, chi-squared

G. Humpire-Mamani, A. J. Traina and C. Traina, "FSCoMS: Feature Selection of Medical Images Based on Compactness Measure from Scatterplots," Healthcare Informatics, Imaging and Systems Biology, IEEE International Conference on(HISB), La Jolla, CA, USA USA, 2012, pp. 86-95.
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