X-ray computed tomography (XCT) datasets with known ground truth pores were developed using realistic XCT simulation. Non-overlapping spherical pores of varying sizes are randomly distributed in a cylindrical part near surfaces and within the core. Ground truth data, ground truth binary data, and reconstructed data for three different signal-to-noise ratios (SNRs) are provided. The data set can be used for evaluation and comparison of image segmentation/detection algorithms.
About this Dataset
| Title | Simulated X-ray computed tomography (XCT) and ground truth images of cylindrical sample with randomly distributed spherical pores |
|---|---|
| Description | X-ray computed tomography (XCT) datasets with known ground truth pores were developed using realistic XCT simulation. Non-overlapping spherical pores of varying sizes are randomly distributed in a cylindrical part near surfaces and within the core. Ground truth data, ground truth binary data, and reconstructed data for three different signal-to-noise ratios (SNRs) are provided. The data set can be used for evaluation and comparison of image segmentation/detection algorithms. |
| Modified | 2022-07-15 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | X-ray computed tomography , defect , flaw , additive manufacturing , image segmentation , evaluation metrics , ground truth data , Non-destructive evaluation |
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"title": "Simulated X-ray computed tomography (XCT) and ground truth images of cylindrical sample with randomly distributed spherical pores",
"description": "X-ray computed tomography (XCT) datasets with known ground truth pores were developed using realistic XCT simulation. Non-overlapping spherical pores of varying sizes are randomly distributed in a cylindrical part near surfaces and within the core. Ground truth data, ground truth binary data, and reconstructed data for three different signal-to-noise ratios (SNRs) are provided. The data set can be used for evaluation and comparison of image segmentation\/detection algorithms.",
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"theme": [
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