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.
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