Adaptive Filtering for Breast Computed Tomography: An Improvement on Current Segmentation Methods for Creating Virtual Breast Phantoms

Computerized breast phantoms have been popular for low-cost alternatives to collecting clinical data by combing them with highly realistic simulation tools. Image segmentation of three-dimensional breast computed tomography (bCT) data is one method to create such phantoms, but requires multiple image processing steps to accurately classify the tissues within the breast. One key step in our segmentation routine is the use of a bilateral filter to smooth homogeneous regions, preserve edges and thin structures, and reduce the sensitivity of the voxel classification to noise corruption. In previous work, the well-known process of bilateral filtering was completed on the entire bCT volume with the primary goal of reducing the noise in the entire volume. In order to improve on this method, knowledge of the varying bCT noise in each slice was used to adaptively increase or decrease the filtering effect as a function of distance to the chest wall. Not only does this adaptive bilateral filter yield thinner structures in the segmentation result but is adaptive on a case-by-case basis, allowing for easy implementation with future virtual phantom generations.