A solid texture analysis based on three-dimensional convolution kernels
Motofumi T. Suzuki, Yoshitomo Yaginuma · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
This paper describes techniques for analyzing 3D volume data by using extended Laws' convolution kernels. Laws' kernels are well known for 2D texture analysis, and have been used for various pattern recognition applications. Although typical Laws' convolution kernels are represented in 2D masks, we have extended the kernels to form 3D masks. The three dimensional extension of the masks allows a pattern recognition system to handle 3D volume data, whereas a traditional approach can handle only 2D image data. Also our approach can be extended for use with various lengths of kernels to generate multiple resolutions of masks. In our experiments, mask resolutions of 3 × 3 × 3, 5 × 5 × 5, 7 × 7 × 7, and 9 × 9 × 9 were tested.