Voxel-wise Weighted MR Image Enhancement using an Extended Neighborhood Filter

TL;DR

This paper introduces a novel edge-preserving, denoising filter for MR images that enhances features in regions of interest by using an extended neighborhood approach, improving upon existing non-linear filters.

Contribution

The paper proposes a new voxel-wise weighted filtering method utilizing extended neighborhood directions for improved MR image enhancement, especially in small or outlying regions.

Findings

Effective in preserving edges and features in MR images.

Outperforms traditional non-linear diffusion filters in tests.

Validated on simulated and clinical MR data.

Abstract

We present an edge preserving and denoising filter for enhancing the features in images, which contain an ROI having a narrow spatial extent. Typical examples include angiograms, or ROI spatially distributed in multiple locations and contained within an outlying region, such as in multiple-sclerosis. The filtering involves determination of multiplicative weights in the spatial domain using an extended set of neighborhood directions. Equivalently, the filtering operation may be interpreted as a combination of directional filters in the frequency domain, with selective weighting for spatial frequencies contained within each direction. The advantages of the proposed filter in comparison to specialized non-linear filters, which operate on diffusion principle, are illustrated using numerical phantom data. The performance evaluation is carried out on simulated images from BrainWeb database for multiple-sclerosis, acute ischemic stroke using clinically acquired FLAIR images and MR angiograms.