Artif Intell Med - Texture feature ranking with relevance learning to classify interstitial lung disease patterns.

Tópicos

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Resumo

JECTIVE: The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images.METHODOLOGY: After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf).RESULTS: For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p<0.05) for many texture feature sets compared to the MI approach. For kNN, RanForest, and SVMrbf, some of these feature subsets had a significantly better classification performance when compared to the set consisting of all features (p<0.05).CONCLUSION: While this approach estimates the relevance of single features, future considerations of GMLVQ should include the pairwise correlation for the feature ranking, e.g. to reduce the redundancy of two equally relevant features.

Resumo Limpo

jectiv general matrix learn vector quantiz gmlvq use estim relev textur featur abil classifi interstiti lung diseas pattern highresolut comput tomographi imagesmethodolog stochast gradient descent gmlvq algorithm provid discrimin distanc measur relev factor can account pairwis correl differ textur featur import classif healthi diseas pattern textur featur extract graylevel cooccurr matric glcms featur rank select accord relev obtain gmlvq comparison mutual inform mi criteria classif perform differ featur subset calcul knearestneighbor knn random forest classifi ranforest support vector machin linear radial basi function kernel svmlin svmrbfresult classifi featur set select relev rank assess gmlvq signific better classif perform p mani textur featur set compar mi approach knn ranforest svmrbf featur subset signific better classif perform compar set consist featur pconclus approach estim relev singl featur futur consider gmlvq includ pairwis correl featur rank eg reduc redund two equal relev featur

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