A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images

Abstract

Denoising and contrast enhancement play key roles in optimizing the trade-off between image quality and X-ray dose. However, these tasks present multiple challenges raised by noise level, low visibility of fine anatomical structures, heterogeneous conditions due to different exposure parameters, and patient characteristics. This work proposes a new method to address these challenges. We first introduce a patch-based filter adapted to the properties of the noise corrupting X-ray images. The filtered images are then used as oracles to define non parametric noise containment maps that, when applied in a multiscale contrast enhancement framework, allow optimizing the trade-off between improvement of the visibility of anatomical structures and noise reduction. A significant amount of tests on both phantoms and clinical images has shown that the proposed method is better suited than others for visual inspection for diagnosis, even when compared to an algorithm used to process low dose images in clinical routine.

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

“…Medical imaging uses many different methods such as magnetic resonance (MR) imaging [1][2][3][4][5][6][7][8], radiography [4,[9][10][11], radionuclide [8,12], optical [11,13,14], ultrasound [1,15] and medical robotics [16,17]. The typical medical imaging system consists of three components (Figure 1): data acquisition, data consolidation and data processing.…”

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

“…They gathered similar patches globally. Irrera et al [28] adapted NL-Means for denoising X-ray images (XNL-Means), and then they applied an additional multi-scale contrast enhancement in the frequency domains.…”

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

“…The second limitation is that most RPCA-based image decomposition imposes the foreground component being pixel-wisely sparse (e.g., L 1 -norm for the sparsity) and the background component being globally low-rank without locally considering the complex spatially varying noise in observation data. However, an observation of low dose X-ray imaging is not only badly corrupted by spatially varying signal-dependent Poisson noise [53,54], but also of low contrast and low SNR between the noise and the signal. This serious signal-dependent noise locally affects every entry of the data matrix and results in unsatisfying foreground vessel images containing many artifact residuals.…”

“…To further remove these spatially varying noisy artifacts from the low contrast foreground vessel, the importance of vessel details in the foreground image sequences should be highlighted. Recently, reducing noise while preserving the visually important image details have attracted increasing attention in noisy image enhancement [53,57] and vessel image segmentation [58]. Specifically, by exploiting joint enhancement and denoising strategy, the desirable vessel extraction method can preserve the feature detail of foreground vessels to accurately recover the vessel structures with the noisy artifacts being removed simultaneously.…”

“…This X-ray attenuation sum model is then perfectly fitted into the low-rank and sparse decomposition model and justify the above-mentioned foreground vessel extraction strategy via completion and subtraction of the background layers from the whole XCA images. There are some X-ray image segmentation [27,59] and denoising [53] applications for the X-ray attenuation decomposition in computer-aided diagnosis and intervention. To the best of our knowledge, the proposed method is the first work to precisely fit such X-ray attenuation decomposition into the low-rank and sparse decomposition for accurately extracting vessels’ shapes and intensities from the complex and noisy backgrounds in XCA images.…”