The paper ‘3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms’ by Dicente et al. has been accepted for publication at IEEE Transactions on Image Processing.
Many image acquisition techniques used in biomedical imaging, material analysis, or structural geology are capable to acquire 3–D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3–D content. One of the most common methods to analyze 3–D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3–D. Current state–of–the–art techniques face many challenges when working with 3–D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3–D Riesz–wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and to compare textures with arbitrary (local) orientations. This paper compares three local alignment criteria for higher–order 3–D Riesz–wavelet transforms. The estimations of local texture orientations are based on higher–order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3–D solid textures with alterations (e.g., rotations, noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors by up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which are better than the other techniques tested on the same database.