3D measurements in conventional X-ray imaging with RGB-D sensors

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

doi:10.1016/j.medengphy.2017.01.024

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…In this approach, deriving the patient's volume relies on the techniques and methods that are described in other research works [31]- [33] related to patient position estimation and movement tracking in clinical settings. Here, we specifically apply a modern surface reconstruction algorithm, KinectFusion, which operates on the point clouds and depth data obtained from the patient.…”

Section: A Rotating Patient One Sensor and Dense Surface Mappingsmentioning

confidence: 99%

Densitometric Radiographic Imaging With Contour Sensors

2019

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We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D S) or bone-enhanced (D B) figures. In the D S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D B type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination. INDEX TERMS Conventional X-ray imaging, contour data, densitometric images, dynamic range, depth information.

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Section: A Rotating Patient One Sensor and Dense Surface Mappingsmentioning

confidence: 99%

Densitometric Radiographic Imaging With Contour Sensors

2019

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“…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

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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.

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3D measurements in conventional X-ray imaging with RGB-D sensors

Cited by 2 publications

References 22 publications

Densitometric Radiographic Imaging With Contour Sensors

Densitometric Radiographic Imaging With Contour Sensors

Volume Data Denoising via Extended Weighted Least Squares

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