A Novel Surface Registration Algorithm With Biomedical Modeling Applications

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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S3Reg: Superfast Spherical Surface Registration Based on Deep Learning

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2021.3069645 ◽

2021 ◽

pp. 1-1

Author(s):

Fenqiang Zhao ◽

Zhengwang Wu ◽

Fan Wang ◽

Weili Lin ◽

Shunren Xia ◽

...

Keyword(s):

Deep Learning ◽

Spherical Surface ◽

Surface Registration

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Remote Sensing Image Registration Algorithm Based on Multi-scale Corner and Haar Transformation

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/300/2/022165 ◽

2019 ◽

Vol 300 ◽

pp. 022165

Author(s):

Yanli Yang ◽

Zhiguo Liu ◽

Jinsheng Zhang

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Remote Sensing Image ◽

Multi Scale ◽

Registration Algorithm ◽

Image Registration Algorithm

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Automatic 3D dense phenotyping provides reliable and accurate shape quantification of the human mandible

Scientific Reports ◽

10.1038/s41598-021-88095-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pieter-Jan Verhelst ◽

H. Matthews ◽

L. Verstraete ◽

F. Van der Cruyssen ◽

D. Mulier ◽

...

Keyword(s):

Repeated Measures ◽

Intraclass Correlation ◽

Three Dimensional ◽

Correlation Coefficients ◽

Surface Registration ◽

Anatomical Landmarks ◽

Centroid Size ◽

Intraclass Correlation Coefficients ◽

Total Variability ◽

Automatic Phenotyping

AbstractAutomatic craniomaxillofacial (CMF) three dimensional (3D) dense phenotyping promises quantification of the complete CMF shape compared to the limiting use of sparse landmarks in classical phenotyping. This study assesses the accuracy and reliability of this new approach on the human mandible. Classic and automatic phenotyping techniques were applied on 30 unaltered and 20 operated human mandibles. Seven observers indicated 26 anatomical landmarks on each mandible three times. All mandibles were subjected to three rounds of automatic phenotyping using Meshmonk. The toolbox performed non-rigid surface registration of a template mandibular mesh consisting of 17,415 quasi landmarks on each target mandible and the quasi landmarks corresponding to the 26 anatomical locations of interest were identified. Repeated-measures reliability was assessed using root mean square (RMS) distances of repeated landmark indications to their centroid. Automatic phenotyping showed very low RMS distances confirming excellent repeated-measures reliability. The average Euclidean distance between manual and corresponding automatic landmarks was 1.40 mm for the unaltered and 1.76 mm for the operated sample. Centroid sizes from the automatic and manual shape configurations were highly similar with intraclass correlation coefficients (ICC) of > 0.99. Reproducibility coefficients for centroid size were < 2 mm, accounting for < 1% of the total variability of the centroid size of the mandibles in this sample. ICC’s for the multivariate set of 325 interlandmark distances were all > 0.90 indicating again high similarity between shapes quantified by classic or automatic phenotyping. Combined, these findings established high accuracy and repeated-measures reliability of the automatic approach. 3D dense CMF phenotyping of the human mandible using the Meshmonk toolbox introduces a novel improvement in quantifying CMF shape.

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