Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Band Selection in Multispectral Images by Minimization of Dependent Information
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Theoretical analysis of multispectral image segmentation criteria
IEEE Transactions on Image Processing
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Multispectral microscopy for applications in histology and cytology has attracted much attention in recent years. It has been shown that the unique transmission spectra of biological tissue provides additional information that is potentially useful for better classification of the pathologies. However, irrelevant features in multispectral data may affect results and performance of data analysis methods. In this paper, a fast band selection method is proposed to increase the contrast-to-noise ratio such that relevant information is maximized while reducing the number of spectral bands. The new method obtains an optimal selection of bands by solving a specific objective function with low computational costs. A thresholding criterion is developed to decide on the number of bands to be selected.