22,304 research outputs found
Endoscopic inspection using a panoramic annular lens
The objective of this one year study was to design, build, and demonstrate a prototype system for cavity inspection. A cylindrical view of the cavity interior was captured in real time through a compound lens system consisting of a unique panoramic annular lens and a collector lens. Images, acquired with a digitizing camera and stored in a desktop computer, were manipulated using image processing software to aid in visual inspection and qualitative analysis. A detailed description of the lens and its applications is given
Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review
Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling
Acquiring reliable microstructure datasets is a pivotal step toward the
systematic design of materials with the aid of integrated computational
materials engineering (ICME) approaches. However, obtaining three-dimensional
(3D) microstructure datasets is often challenging due to high experimental
costs or technical limitations, while acquiring two-dimensional (2D)
micrographs is comparatively easier. To deal with this issue, this study
proposes a novel framework for 2D-to-3D reconstruction of microstructures
called Micro3Diff using diffusion-based generative models (DGMs). Specifically,
this approach solely requires pre-trained DGMs for the generation of 2D
samples, and dimensionality expansion (2D-to-3D) takes place only during the
generation process (i.e., reverse diffusion process). The proposed framework
incorporates a new concept referred to as multi-plane denoising diffusion,
which transforms noisy samples (i.e., latent variables) from different planes
into the data structure while maintaining spatial connectivity in 3D space.
Furthermore, a harmonized sampling process is developed to address possible
deviations from the reverse Markov chain of DGMs during the dimensionality
expansion. Combined, we demonstrate the feasibility of Micro3Diff in
reconstructing 3D samples with connected slices that maintain morphologically
equivalence to the original 2D images. To validate the performance of
Micro3Diff, various types of microstructures (synthetic and experimentally
observed) are reconstructed, and the quality of the generated samples is
assessed both qualitatively and quantitatively. The successful reconstruction
outcomes inspire the potential utilization of Micro3Diff in upcoming ICME
applications while achieving a breakthrough in comprehending and manipulating
the latent space of DGMs
Focal Spot, Spring 1998
https://digitalcommons.wustl.edu/focal_spot_archives/1078/thumbnail.jp
TM digital image products for applications
The image characteristics of digital data generated by LANDSAT 4 thematic mapper (TM) are discussed. Digital data from the TM resides in tape files at various stages of image processing. Within each image data file, the image lines are blocked by a factor of either 5 for a computer compatible tape CCT-BT, or 4 for a CCT-AT and CCT-PT; in each format, the image file has a different format. Nominal geometric corrections which provide proper geodetic relationships between different parts of the image are available only for the CCT-PT. It is concluded that detector 3 of band 5 on the TM does not respond; this channel of data needs replacement. The empty bin phenomenon in CCT-AT images results from integer truncations of mixed-mode arithmetric operations
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks
In digital rock physics, analysing microstructures from CT and SEM scans is
crucial for estimating properties like porosity and pore connectivity.
Traditional segmentation methods like thresholding and CNNs often fall short in
accurately detailing rock microstructures and are prone to noise. U-Net
improved segmentation accuracy but required many expert-annotated samples, a
laborious and error-prone process due to complex pore shapes. Our study
employed an advanced generative AI model, the diffusion model, to overcome
these limitations. This model generated a vast dataset of CT/SEM and binary
segmentation pairs from a small initial dataset. We assessed the efficacy of
three neural networks: U-Net, Attention-U-net, and TransUNet, for segmenting
these enhanced images. The diffusion model proved to be an effective data
augmentation technique, improving the generalization and robustness of deep
learning models. TransU-Net, incorporating Transformer structures, demonstrated
superior segmentation accuracy and IoU metrics, outperforming both U-Net and
Attention-U-net. Our research advances rock image segmentation by combining the
diffusion model with cutting-edge neural networks, reducing dependency on
extensive expert data and boosting segmentation accuracy and robustness.
TransU-Net sets a new standard in digital rock physics, paving the way for
future geoscience and engineering breakthroughs
Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL
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