22-th International Conference on Computer Graphics GraphiCon'2012
Page(s)
68-72
Address
Moscow, Russia
Abstract
Noise in 3D computer tomography (CT) images is close to white and becomes large when patient radiation doses are reduced. We propose two methods for noise reduction in CT images: 3D extension of fast rank algorithms (Rank-2.5D) and 3D extension of a non-local means algorithm (NLM-2.5D). We call both our algorithms “2.5D” because the 3-dimensional NLM algorithm is slightly asymmetric by slice axes, while our Rank algorithms, being fully symmetric mathematically and by results, have some implementation asymmetry. A comparison of the methods is presented. It is shown that NLM-2.5D method has the best quality, but is very computationally expensive: its complexity quickly rises as a function of the neighborhood size, while Rank-2.5D only shows a linear growth. Artificial test sequences are used for signal-to-noise performance measurements, while real CT scans are used for visual assessment of results.
