Three-dimensional Region-based Filters for Noise Removal in Volumetric Data

Abstract

We present region-based filters for noise removal in three-dimensional (3D) images in the (x, y, z) domain. The filters start with region growing at each voxel to determine a context-dependent region in 3D. Appropriate selection of region-growing criteria provides regions that approximate 3D objects or features present in the image. Local statistics are then used to filter noise. The method permits adaptive noise removal without degradation of edges, surfaces, or shapes of objects. Results of 3D region-based mean, median, and local linear minimum mean-squared error (LLMMSE) filtering are shown, along with results of two-dimensional (2D) region-based filtering on a slice-by-slice basis, as well as 2D and 3D fixed-neighborhood filtering. Results of 3D region-based filtering possess lower mean-squared errors (MSE) than the results of fixed-neighborhood filters in 3D or 2D. Furthermore, results of 3D region-based filtering possess MSE lower than or equal to the MSE of the results of 2D region-based filtering because 3D filtering exploits the presence of 3D structures in the images. The methods should be applicable in processing of 3D medical images and as a pre-processing step in volume visualization.

Indexing terms:

• Three-dimensional image filtering
• Volumetric data filtering
• Noise filtering
• Region-based filtering
• Local statistics-based filters