Optimized Steerable Wavelets for Texture Analysis of Lung Tissue In 3-D CT: Classification of Usual Interstitial Pneumonia

Our aim is to optimize wavelet-based feature extraction for differentiating between the classical versus atypical pattern of usual interstitial pneumonia (UIP) in volumetric CT. Our proposal is to act on the bandwidth of steerable wavelets while maintaining their tight frame property. To that end, we designed a family of maximally localized wavelet pyramids in 3-D for a continuously adjustable radial bandwidth [Ω, π], Ω ∈ [π∕4, π∕2]. The proposed wavelets are coupled with a rotation-covariant directional operator based on the Riesz transform, which provides characterizations of the organization image directions independently from their local orientations. The influence of the wavelet bandwidth on the classification performance was found to be large with area under the receiver operating characteristic curve (AUC) values in [0.784, 0.921]. This demonstrated the importance of finding the minimum spatial support of the wavelet required to leverage the wealth of morphological tissue properties in the vicinity of the lung boundaries

Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: classification of usual interstitial pneumonia ::classification of usual interstitial pneumonia

Our aim is to optimize wavelet–based feature extraction for differentiating between the classical versus atypical pattern of usual interstitial pneumonia (UIP) in volumetric CT. Our proposal is to act on the bandwidth of steerable wavelets while maintaining their tight frame property. To that end, we designed a family of maximally localized wavelet pyramids in 3–D for a continuously adjustable radial bandwidth [ ; ], 2 [=4; =2]. The proposed wavelets are coupled with a rotation–covariant directional operator based on the Riesz transform, which provides characterizations of the organization image directions independently from their local orientations. The influence of the wavelet bandwidth on the classification performance was found to be large with area under the receiver operating characteristic curve (AUC) values in [0:784; 0:921]. This demonstrated the importance of finding the minimum spatial support of the wavelet required to leverage the wealth of morphological tissue properties in the vicinity of the lung boundaries