Medical X-ray image enhancement by intra-image and inter-image similarity

In medical X-ray examinations, images suffer considerably from severe, signal-dependent noise as a result of the effort to keep applied doses as low as possible. This noise can be seen as an additive signal that degrades image quality and might disguise valuable content. Lost information has to be restored in a post-processing step. The crucial aspect of filtering medical images is preservation of edges and texture on the one hand and removing noise on the other hand. Classical smoothing filters, such as Gaussian or box filtering. are data-independent and equally blur the image content. State-of-the-art methods currently make use of local neighborhoods or global image statistics. However, exploiting global self-similarity within an image and inter-image similarity for subsequent frames of a sequence bears an unused potential for image restoration. We introduce a non-local filter with data-dependent response that closes the gap between local filtering and stochastic methods. The filter is based on the non-local means approach proposed by Buades1 et al. and is similar to bilateral filtering. In order to apply this approach to medical data, we heavily reduce the computational costs incurred by the original approach. Thus it is possible to interactively enhance single frames or selected regions of interest within a sequence. The proposed filter is applicable for time-domain filtering without the need for accurate motion estimation. Hence it can be seen as a general solution for filtering 2D as well as 2D+t X-ray image data.

Subjects

Denoising

Fluoroscopy

Image enhancement

Image restoration

Self-Similarity

X-ray

DDC Class

610: Medizin

Options

Medical X-ray image enhancement by intra-image and inter-image similarity