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Use of Microcomputed Tomography and Image Processing Tools in Medicinal and Aromatic Plants
Published in Amit Baran Sharangi, K. V. Peter, Medicinal Plants, 2023
Yogini S. Jaiswal, Yanling Xue, Tiqiao Xiao, Leonard L. Williams
After scanning of samples, the 2D images are reconstructed into 3D images. Reconstruction involves the creation of 3D datasets from the 2D projections. Reconstruction is also referred to as visualization. During the reconstruction process, every voxel is mapped, and from different angles, the projections of the same voxel are created. The mapping is carried out by using an algorithm that is inbuilt in the commercially available software. The algorithm used in such software is called the Feldkamp filtered back-projection algorithm. Several software is commercially available, and these include: General Electric, Datos, Avizo®, Amira, Solid works, Blender, Octopus Reconstruction, etc. Some software in addition to the inbuilt algorithm, provide modules which can be applied in various kinds of image analysis goals. The volumetric data are significantly different from a computer aided design (CAD) software and require high power computers for processing and storage.
Fluorescent Analysis Technique
Published in Victoria Vladimirovna Roshchina, Fluorescence of Living Plant Cells for Phytomedicine Preparations, 2020
Victoria Vladimirovna Roshchina
Autofluorescence from ablating root tissue by UV wavelengths has been recorded in video mode at a rate of 30 frames per second at 1080 pixels with white balance and aperture manually adjusted to optimize the visualization of features of interest. Image stacks have been extracted from these videos, and root anatomical features as well as colonizing organisms have been segmented from image stacks by thresholding based on red, green, and blue spectra with the MIPAR™ software. The spectral values used for thresholding were determined in images by outlining the feature of interest and utilizing maximum and minimum spectral values in the red, green, and blue channels of pixels comprising that feature. Manual review of thresholder images was performed to verify correct segmentation. The original and segmented images have been used for three-dimensional reconstruction and quantification of the root segment and organisms using Avizo 9 Lite software (VSG Inc., Burlington, MA). A new method for high-throughput, three-dimensional phenotyping of roots could enable large screens and genetic studies to characterize host–pathogen interactions. To gain a better understanding of the functional role and genetic control of the interaction between soil organisms and root anatomy, it would be useful to develop high-throughput methods that can spatially quantify and qualify colonization of roots by soil biota in three dimensions.
Use of electron microscopy to study megakaryocytes
Published in Platelets, 2020
Cyril Scandola, Mathieu Erhardt, Jean-Yves Rinckel, Fabienne Proamer, Christian Gachet, Anita Eckly
One challenge of volume EM is to deal with large datasets. The computers must be powerful enough to analyze and store these data (Microsoft Windows 7/8/10-64 bits, 16 GB of RAM and a graphic card NVIDIA with at least 2 GB dedicated to memory). The images are loaded into the processing program, stitched together and aligned to perfectly match into each other. Segmentation is then performed, i.e., the outlines of the different subcellular structures are manually delimited. Semi-automatic tracing methods are at present the only way to reconstruct some MK features, e.g., the DMS. Several groups and companies have developed software to facilitate 3D and correlative imaging, such as Amira and Avizo (Visualization Sciences Group and FEI Company), IMOD (Boulder Laboratory, Colorado), MAPS (FEI), or TomoJ (Sergio’s group at the Institut Curie, Paris).
Three-dimensional multimodality fusion imaging as an educational and planning tool for deep-seated meningiomas
Published in British Journal of Neurosurgery, 2018
Mitsuru Sato, Kensuke Tateishi, Hidetoshi Murata, Taichi Kin, Jun Suenaga, Hajime Takase, Tomohiro Yoneyama, Toshiaki Nishii, Ukihide Tateishi, Tetsuya Yamamoto, Nobuhito Saito, Tomio Inoue, Nobutaka Kawahara
One neurosurgical resident (M.S.) constructed 3D-MFIs in all cases. All of the image data were obtained in DICOM format, and subsequently transferred and processed using the Avizo software program (version 6.0, Visage Imaging, CA). The threshold level, opacity curve, and color map for segmentation of anatomical structures were controlled in all cases. Briefly, all images were automatically fused with a normalized mutual information technique.18 The vessels derived from the MRA and DSA were rendered with simple thresholding. The tumor and the dura derived from the CE-THRIVE images were also rendered with simple thresholding. The brain and nerves segmented from the DRIVE images were rendered with manual labeling. The surface rendering method was used for all the modalities. Subsequently, rendered images were calibrated using the CT images. Finally, 3D-MFIs were constructed from these segmentations. After processing, 3D-MFIs were interactively transformed as follows:1 cut the virtual skull as in a craniotomy,2 transform the brain surface as though it were retracted brain, and3 make virtual structures transparent to assess the region behind the tumor. 3D-MFIs were captured in a CAD application (Adobe Acrobat 9 Pro Extended, CA), and then converted to PDF format. 3D-MFIs were freely movable by examiner using Adobe Acrobat 9 Pro Extended (Figure 1, video 1). VR videos were created in Avizo software.
Morphological characterization of defects in all-ceramic crown adhesive layer
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
Y. Yasothan, K. Shindo, N. Schmitt, E. Vennat
In AVIZO, the segmentation workflow consists in linking each voxel of the 3D images to its dental assembly component. This connection was done manually for large components of dental assemblies using a local thresholding tool that selects all voxels around a given point whose gray level is within an user-defined threshold interval. For roughly spherical shaped defects with an average diameter inferior to 36.5 μm, the selection was carried out using a black top hat filter. To capture flat shaped defects with thickness less than 36.5 μm, a fictive component containing all the voxels forming the crown boundary layer was created. This layer thickness was 36.5 μm. Then, by thresholding this part, the voxels corresponding to these defects were selected.