Automatic identification of three-dimensional morphometric features of vertebrae

For 3D digital measurement of large scale objects, image mosaic is the key technology to achieve whole measurement for a small measuring field of the sensor unit. A viscous-target-based three-dimensional image mosaic technology is proposed. The screw theory is introduced to realize the spatial image mosaic. The method permits an automatic identification of targets and a better matching for the feature coded technology. In experiments, the method was proved to be valid, with a relatively high precision on three-dimensional image mosaic. The relative error of the space length measurement is less than 0.6%.

Download Full-text

Automatic Identification of First-Order Veins and Corolla Contours in Three-Dimensional Floral Images

Frontiers in Plant Science ◽

10.3389/fpls.2020.549699 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yi-Hsiang Wang ◽

Hao-Chun Hsu ◽

Wen-Chieh Chou ◽

Chia-Hao Liang ◽

Yan-Fu Kuo

Keyword(s):

Three Dimensional ◽

Automatic Identification ◽

First Order

Download Full-text

Development and Verification of the Capacity Curve for Two Dimensional Reinforced Concrete Moment-Resisting Frames System under Earthquake Loading

Journal of Engineering ◽

10.31026/j.eng.2021.06.06 ◽

2021 ◽

Vol 27 (6) ◽

pp. 73-96

Author(s):

Haider A Abass ◽

Husain Khalaf Jarallah

Keyword(s):

Reinforced Concrete ◽

Plastic Hinge ◽

Pushover Analysis ◽

Three Dimensional ◽

Automatic Identification ◽

Two Dimensional ◽

Seismic Evaluation ◽

Concrete Frames ◽

Capacity Curves ◽

Comparison Of The Results

Pushover analysis is an efficient method for the seismic evaluation of buildings under severe earthquakes. This paper aims to develop and verify the pushover analysis methodology for reinforced concrete frames. This technique depends on a nonlinear representation of the structure by using SAP2000 software. The properties of plastic hinges will be defined by generating the moment-curvature analysis for all the frame sections (beams and columns). The verification of the technique above was compared with the previous study for two-dimensional frames (4-and 7-story frames). The former study leaned on automatic identification of positive and negative moments, where the concrete sections and steel reinforcement quantities the source of these moments. The comparison of the results between the two methodologies was carried out in terms of capacity curves. The results of the conducted comparison highlighted essential points. It was included the potential differences between default and user-defined hinge properties in modeling. The effect of the plastic hinge length and the transverse of shear reinforcement on the capacity curves was also observed. Accordingly, it can be considered that the current methodology in this paper more logistic in the representation of two and three-dimensional structures.

Download Full-text

Proposition of local automatic algorithm for landmark detection in 3D cephalometry

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i2.1827 ◽

2021 ◽

Vol 10 (2) ◽

pp. 707-715

Author(s):

Mohammed Ed-dhahraouy ◽

Hicham Riri ◽

Manal Ezzahmouly ◽

Abdelmajid El Moutaouakkil ◽

Hakima Aghoutan ◽

...

Keyword(s):

Three Dimensional ◽

Automatic Identification ◽

Local Geometry ◽

3D Images ◽

Mean Error ◽

Landmark Identification ◽

Volume Of Interest ◽

Intensity Information ◽

3D Cephalometry ◽

Geometrical Information

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.

Download Full-text

Automatic Identification and Validation of Planar Collagen Organization in the Aorta Wall with Application to Abdominal Aortic Aneurysm

Microscopy and Microanalysis ◽

10.1017/s1431927613013251 ◽

2013 ◽

Vol 19 (6) ◽

pp. 1395-1404 ◽

Cited By ~ 17

Author(s):

Stanislav Polzer ◽

T. Christian Gasser ◽

Caroline Forsell ◽

Hana Druckmüllerova ◽

Michal Tichy ◽

...

Keyword(s):

Arterial Wall ◽

Three Dimensional ◽

Polarized Light ◽

Vascular Diseases ◽

Aortic Aneurysms ◽

Fast Fourier Transformation ◽

Automatic Identification ◽

Collagen Organization ◽

Aorta Wall ◽

Abdominal Aortic

AbstractArterial physiology relies on a delicate three-dimensional (3D) organization of cells and extracellular matrix, which is remarkably altered by vascular diseases like abdominal aortic aneurysms (AAA). The ability to explore the micro-histology of the aorta wall is important in the study of vascular pathologies and in the development of vascular constitutive models, i.e., mathematical descriptions of biomechanical properties of the wall. The present study reports and validates a fast image processing sequence capable of quantifying collagen fiber organization from histological stains. Powering and re-normalizing the histogram of the classical fast Fourier transformation (FFT) is a key step in the proposed analysis sequence. This modification introduces a powering parameterw, which was calibrated to best fit the reference data obtained using classical FFT and polarized light microscopy (PLM) of stained histological slices of AAA wall samples. The values ofw= 3 and 7 give the best correlation (Pearson's correlation coefficient larger than 0.7,R2about 0.7) with the classical FFT approach and PLM measurements. A fast and operator independent method to identify collagen organization in the arterial wall was developed and validated. This overcomes severe limitations of currently applied methods like PLM to identify collagen organization in the arterial wall.

Download Full-text

Investigation of Cellular Confinement in Three-Dimensional Microscale Fibrous Substrates: Fabrication and Metrology

Journal of Micro and Nano-Manufacturing ◽

10.1115/1.4038803 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Filippos Tourlomousis ◽

William Boettcher ◽

Houzhu Ding ◽

Robert C. Chang

Keyword(s):

Cell Fate ◽

Cell Shape ◽

Shape Representation ◽

Three Dimensional ◽

Petri Dish ◽

Writing System ◽

Morphometric Features ◽

Confocal Fluorescence ◽

Primary Fibroblasts ◽

Confocal Fluorescence Imaging

Engineered microenvironments along with robust quantitative models of cell shape metrology that can decouple the effect of various well-defined cues on a stem cell's phenotypic response would serve as an illuminating tool for testing mechanistic hypotheses on how stem cell fate is fundamentally regulated. As an experimental testbed to probe the effect of geometrical confinement on cell morphology, three-dimensional (3D) poly(ε-caprolactone) (PCL) layered fibrous meshes are fabricated with an in-house melt electrospinning writing system (MEW). Gradual confinement states of fibroblasts are demonstrated by seeding primary fibroblasts on defined substrates, including a classical two-dimensional (2D) petri dish and porous 3D fibrous substrates with microarchitectures tunable within a tight cellular dimensional scale window (1–50 μm). To characterize fibroblast confinement, a quantitative 3D confocal fluorescence imaging workflow for 3D cell shape representation is presented. The methodology advanced allows the extraction of cellular and subcellular morphometric features including the number, location, and 3D distance distribution metrics of the shape-bearing focal adhesion (FA) proteins.

Download Full-text