Measuring granule phase volume distributions using X-ray microtomography

Dale, Steven; Wassgren, Carl; Litster, James D.

doi:10.1016/j.powtec.2014.06.009

Cited by 18 publications

(7 citation statements)

References 30 publications

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“…To avoid problems that some materials pose for gray value-based segmentations, several works have used combinations of the watershed transform [ 7 ] and a distance transform [ 8 ] to segment objects in contact based on their shape: soil particles [ 9 – 13 ] and glass beads [ 14 , 15 ], but also biological objects, such as clustered nuclei [ 16 ] and neuron somata [ 17 ]. Some aspects of tesserae, however, complicate their segmentation via conventional shape-based methods that use a 3D distance transform to segment objects according to their geometry.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage

et al. 2017

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IntroductionMany biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays.MethodsBesides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size.ResultsEach of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage

et al. 2017

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“…The material and sieve preparation will be referenced using the letter and number combinations in Table 1, such as Powder A1 or Powder B3. The analysis of XRCT granule images was performed using ImageJ and MATLAB based on the analysis methodology developed by Dale et al [32]. A binary threshold was applied in ImageJ using the automatic threshold selector to separate the granule from the background and nongranule pixels above the threshold limit are manually removed.…”

Section: Methodsmentioning

confidence: 99%

“…XRCT is instead useful for examining meso-structures in ultrafine powder granules rather than porosity alone, which can be obtained from other methods. Recently published work by Dale et al [32] describes a novel technique for measuring and describing phase distributions in granule microstructure which will be used for this study. The quantitative nature of this technique means that the impact of bed preparation, compaction, or other processing can be measured and compared to see which methods have the greatest impact.…”

Section: Introductionmentioning

confidence: 99%

Microstructure of single-droplet granules formed from ultra-fine powders

Davis¹,

Waibel²,

Wang³

et al. 2017

Powder Technology

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“…In recent decades, X-ray computed tomography (XCT), one of the well-established non-destructive techniques, is being increasingly attractive in packed bed structure investigation for its significant advantages of reconstructing the real threedimensional internal structure of packed bed and directly obtaining the spatial information of structure properties such as granule size, bulk porosity and porosity distribution. [20][21][22] However, XCT technique is seldom applied to the packed bed of iron ore granules, where understanding the threedimensional packed bed structure is of practical significance for improving iron ore sintering yield and quality.…”

Section: Introductionmentioning

confidence: 99%

Taguchi Orthogonal Test on Granule Properties and Porosity Distribution in Sintering Bed using High-resolution X-ray Computed Tomography

Zhou

et al. 2020

ISIJ Int.

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Granulation and packing of iron ores are highly essential for having a strong function with packed bed porosity distribution, and further affecting the subsequent sintering process. In this study, high-resolution X-ray computed tomography technique was applied to investigate the influence of moisture, hydrated lime and concentrate levels on granule properties and porosity distribution of packed bed based on the Taguchi orthogonal array tests, and the optimum granulation factors combination was determined by the defined porosity segregation degree for improving packed bed homogeneity. Moisture was found to be the dominant factor affecting granule size with a major percent contribution of 94.42%. Bulk bed porosity was significantly affected by all three selected factors of moisture, hydrated lime and concentrate levels. The percent contribution order was shown as hydrated lime (55.10%) > moisture (29.12%) > concentrate (14.36%). The whole packed bed was found to exhibit significant inhomogeneity. Axial porosity increases from the bottom upwards along the packed bed height, and radial porosity appears a symmetric parabolic distribution where porosity achieves the minimum of 0.3 at bed center and increases sharply near the wall. To achieve the homogeneous packed bed of iron ore granules, the optimum granulation factors combination for decreasing axial and radial porosity segregation are determined as 6.8% moisture, 4% hydrated lime, 0% concentrate and 5.8% moisture, 4% hydrated lime, 0% concentrate, respectively. The results provide the theoretical guidance for granulation and packing in iron ore sintering to improve sintering yield and quality.

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Measuring granule phase volume distributions using X-ray microtomography

Cited by 18 publications

References 30 publications

Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage

Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage

Microstructure of single-droplet granules formed from ultra-fine powders

Taguchi Orthogonal Test on Granule Properties and Porosity Distribution in Sintering Bed using High-resolution X-ray Computed Tomography

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