Computer aided diagnosis of diffuse lung disease in multi-detector CT - Selecting 3D texture features

Computed Tomography (CT) is the modality of choice for the diagnosis of Diffuse Lung Disease (DLD) affecting lung parenchyma. The need for Computer Aided Diagnosis (CAD) schemes aimed at DLD patterns quantification in lung CT, originates from large inter- and intra observer variability characterizing DLD interpretation. The majority of the proposed CAD schemes aimed at DLD characterization exploits textural features combined with supervised classification algorithms. However, the exploitation of these features is suboptimal, since no feature reduction or evaluation is performed prior to the classification task. The aim of the current paper is to investigate 3D texture features sets (histogram signatures, co-occurrence and run length matrices' statistics) regarding their capability in DLD patterns' characterization (normal, ground glass, reticular and honeycombing). Earth Mover's Distance (EMD), k-Nearest Neighbor (k-NN) and Multinomial Logistic Regression (MLR) classifiers where used to access the performance of individual feature sets. In the analysis performed Histogram Signature (HS) feature set combined with EMD classifier, achieves the lowest overall accuracy (80.2 %). Co-occurrence based feature set presented the highest overall classification accuracy (99.3 %) when combined with k-NN classifier. However, both Run Length and Co-occurrence based feature sets, presented robustness against classifier choice and higher classification accuracy than HS feature set.