Limited-view binary tomography reconstruction assisted by shape centroid

Lukić, Tibor; Balázs, Péter

doi:10.1007/s00371-020-02044-8

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…The positions and voxel values of these points are extracted by changing the voxel values of this point. Make it have a uniform and distinguishable voxel value with other centroid points) to form a new threedimensional model [37,38], as shown in Figure 8.…”

Section: Skeleton Extractionmentioning

confidence: 99%

Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

Feng,

Zhu,

2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Analysis of magnetic resonance angiography image data is crucial for early detection and prevention of stroke patients. Extracting the 3D Skeleton of cerebral vessels is the focus and difficulty of analysis. OBJECTIVES: The objective is to remove other tissue components from the vascular tissue portion of the image with minimal loss by reading MRA image data and performing processing processes such as grayscale normalization, interpolation, breakpoint detection and repair, and image segmentation to facilitate 3D reconstruction of cerebral blood vessels and the reconstructed vascular tissues make extraction of the Skeleton easier. METHODS: Considering that most of the existing techniques for extracting the 3D vascular Skeleton are corrosion algorithms, machine learning algorithms require high hardware resources, a large number of learning and test cases, and the accuracy needs to be confirmed, an average plane center of mass computation method is proposed, which improves the average plane algorithm by combining the standard plane algorithm and the center of mass algorithm. RESULTS: Intersection points and skeleton breakpoints on the Skeleton are selected as critical points and manually labeled for experimental verification, and the algorithm has higher efficiency and accuracy than other algorithms in directly extracting the 3D Skeleton of blood vessels. CONCLUSION: The method has low hardware requirements, accurate and reliable image data, can be automatically modeled and calculated by Python program, and meets the needs of clinical applications under information technology conditions.

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Section: Skeleton Extractionmentioning

confidence: 99%

Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

Feng,

Zhu,

2023

EAI Endorsed Trans Perv Health Tech

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“…Including prior knowledge about the scanned object into the reconstruction process is a well-known strategy to compensate for limited data in XCT [4][5][6]. A specific type of prior knowledge is exploited in discrete tomography (DT) Daniel Frenkel and Nathanaël Six have contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

“…Fig 6. The rNMP for Tabu-DART and DART with four different choices of p for increasing missing wedge…”

mentioning

confidence: 99%

Tabu-DART: a dynamic update strategy for efficient discrete algebraic reconstruction

et al. 2022

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In X-ray computed tomography, discrete tomography (DT) algorithms have been successful at reconstructing objects composed of only a few distinct materials. Many DT-based methods rely on a divide-and-conquer procedure to reconstruct the volume in parts, which improves their run-time and reconstruction quality. However, this procedure is based on static rules, which introduces redundant computation and diminishes the efficiency. In this work, we introduce an update strategy framework that allows for dynamic rules and increases control for divide-and-conquer methods for DT. We illustrate this framework by introducing Tabu-DART, which combines our proposed framework with the Discrete Algebraic Reconstruction Technique (DART). Through simulated and real data reconstruction experiments, we show that our approach yields similar or improved reconstruction quality compared to DART, with substantially lower computational complexity.

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“…Including prior knowledge about the scanned object into the reconstruction process is a well-known strategy to compensate for limited data in XCT [113,114,115]. A specific type of prior knowledge is exploited in Discrete Tomography (DT) [46], where the object is assumed to be composed of only a few materials.…”

Section: Introductionmentioning

confidence: 99%

Improved X-ray CT reconstruction techniques with non-linear imaging models

Six

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Limited-view binary tomography reconstruction assisted by shape centroid

Cited by 7 publications

References 39 publications

Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

Tabu-DART: a dynamic update strategy for efficient discrete algebraic reconstruction

Improved X-ray CT reconstruction techniques with non-linear imaging models

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