3D measurements from X-ray images and dense surface mappings

Albiol, F.; Corbí, Alberto; Albiol, Alberto

doi:10.1109/gmepe-pahce.2016.7504638

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some volume data (such as Computed tomography(CT) scans, Magnetic resonance imaging (MRI) scans, ultrasound scans, and confocal microscopy volumes) are usually polluted by noise during the process of transmission and collection, for example, ultrasound volumes are usually polluted by speckle noise and MRI may be contaminated by Rician noise. Even using highresolution scanners, noise inevitably appears in the obtained volume data during the data acquisition procedure [2], [3]. Therefore, noise reduction is an important pre-processing step for improving the quality of medical inspection and analytic tasks like volume visualization or segmentation [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Volume Data Denoising via Extended Weighted Least Squares

et al. 2019

View full text Add to dashboard Cite

During the data acquisition procedure, volume data are usually contaminated by noises. This would create visual confusion and misunderstanding in analyzing the volume data. Thus, noise reduction is necessary for improving the quality of volume data inspection and analytic tasks. However, it is far from being fully resolved in removing noise while maximally retaining geometric sharp features. In this paper, we present a powerful volume denoising method based on the extended weighted least squares. We improve the weighted least squares method and extend it to 3D for volume data denoising. The primary advantage of the proposed method is that it can consistently produce better results in removing noise while preserving sharp features. We illustrate our technique on synthetic and real-world 3D data and compare our method with the median method, weighted least squares, L0 volume gradient minimization, and edge aware anisotropic diffusion method. The experimental results demonstrate that our method can achieve higher quality results than the selected state-of-the-art methods.INDEX TERMS Extended weighted least squares, sharp features preservation, volume data denoising.

show abstract