A statistical approach for selecting discriminative features of spatial regions of interest
We propose a statistical approach based on a supervised framework for reducing the dimensionality of the feature space when characterizing and classifying spatial Regions of Interest (ROIs). Our approach employs the statistical techniques of Bootstrapping simulation, Bayesian Inference and Markov Chain Monte Carlo (MCMC), to select the most informative features according to their discriminative power across distinct classes of data. This reduces the dimensionality of the initial feature space and also improves the classification of the ROIs, since features providing irrelevant information with respect to class membership are discarded. We also introduce a weighted Euclidean Distance designed to effectively classify the ROIs. We evaluate the proposed technique using experiments that involve synthetic spatial regions and real ROIs extracted from medical images. We demonstrate its effectiveness in classification experiments (using established classifiers) and in similarity searches. We also test its scalability on large datasets. Our approach is comparable with or better than other major competitors. We achieve an accuracy of 87% on classifying ROIs in brain images. These results are an improvement of previously reported classification experiments, and show the effect of reducing the dimensionality of the initial feature space.
classification region data discriminative feature selection dimensionality reduction medical image analysis