Application of Tomographic 3D Reconstruction Methods To A Diverse Range of Biological Preparations

1990 ◽  
Vol 48 (1) ◽  
pp. 516-517
Author(s):  
B.F. McEwen ◽  
J. Frank
Keyword(s):  
Golgi Apparatus ◽  
3D Reconstruction ◽  
Three Dimensional ◽  
Original Study ◽  
Sample Thickness ◽  
Biological Structure ◽  
Diverse Range ◽  
Selective Staining ◽  
Reconstruction Methods ◽  
Peak Search

Tomography is a completely general three-dimensional (3D) reconstruction approach which can be applied to almost any biological structure which is sufficiently contrasted from its background. We have used tomography to compute 3D reconstructions of a diverse group of biological preparations which include: the cilium and the kinetochore, which were positively stained and imaged in 0.25 μm sections; the dendrite and the Golgi apparatus, which were selectively stained, before embedding, and imaged in 3 μm sections; chromatin fibers, which were negatively stained; and patch-clamped membranes which were critically point dried and imaged as whole mounts in their micropipettes.With the cilium, we found good overall agreement with the known structure but were unable to completely resolve the fine structure for several reasons. Figure 1 illustrates how a different volume rendering technique, which included partial transparency, was able to bring out a dynein repeat that was not detected in our original study. With the kinetochore (figure 2), the structure is too complicated for the eye to readily comprehend and a procedure of selective data reduction is being developed. The dendrite and the Golgi apparatus (figure 3) are favorable objects for tomography because selective staining allows a large depth to be reconstructed (3 μm) and yet the structures are simple enough for surface renditions to provide a clear picture of the 3D ultrastructure. The dendrite reconstructions provided valuable surface area information and the Golgi reconstructions are expected to yield clues as to how this structure is formed. The chromatin fiber afforded the highest resolution reconstruction we obtained (figure 4) and, in conjunction with a 3D peak search, we were able to locate the positions of individual nucleosomes (figure 5). However, fiber flattening and other effects of the preparatory procedures were noted. The cylindrical geometry of the patch-clamped membranes in a micropipette is ideal for tomography because the sample thickness does not change with tilt angle and an unlimited tilt range should be possible. We are currently working on the latter but the availability of the full tilt range and the sides of the micropipette present some problems for the presently implemented alignment schemes.

scholarly journals Three-dimensional analysis of the root canal preparation with Reciproc Blue®, WaveOne Gold® and XP EndoShaper®. A new method in vivo

2020 ◽  
Author(s):  
Javier Caviedes-Bucheli ◽  
Nestor Rios-Osorio ◽  
Diana Usme ◽  
Cristian Jimenez ◽  
Adriana Pinzon ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Root Canal ◽  
Paired Comparison ◽  
Three Dimensional ◽  
New Method ◽  
Root Canal Preparation ◽  
Paired Data ◽  
Reconstruction Methods ◽  
Before And After

Abstract Background: The purpose of this study was to evaluate the changes in canal volume after root canal preparation in vivo with 3 different single-file techniques (Reciproc-Blue®, WaveOne-Gold® and XP-EndoShaper®), with a new method using CBCT and 3D reconstruction. Methods: In this prospective study, thirty human lower premolars from healthy patients were used, in which extraction was indicated for orthodontic reasons. All the teeth used were caries- and restoration-free with complete root development, without signs of periodontal disease or traumatic occlusion, and with only one straight canal (up to 25º curvature). Teeth were randomly divided into three different groups: Reciproc-Blue, WaveOne-Gold and XP-EndoShaper. CBCT scans before root canal preparation were used to create a 3D reconstruction with RHINOCEROS 5.0 software to assess the initial canal volume, and then compared with 3D reconstructions after canal preparation to measure the increase in canal volume. Student’s t test for paired data were used to determine statistically significant differences between the before and after canal volumes. Anova test was used to determine statistically significant differences in the percentage of canal volume increase between the groups and Tukey's post-hoc test were used to paired comparison.Results: Reciproc-Blue showed the higher increase in canal volume, followed by WaveOne-Gold and XP-EndoShaper (p = 0.003). XP-EndoShaper did not show a statistically significant increase in canal volume after root canal preparation (p = 0.06).Conclusion: With this model, Reciproc-Blue showed higher increase in root canal volume, followed by WaveOne-Gold, while XP-EndoShaper did not significantly increase root canal volume during preparation.

scholarly journals Enhancement of RGB-D Image Alignment Using Fiducial Markers

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1497 ◽  
Author(s):  
Tiago Madeira ◽  
Miguel Oliveira ◽  
Paulo Dias
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
3D Models ◽  
Difficult Problem ◽  
Image Alignment ◽  
Fiducial Markers ◽  
Final Model ◽  
Optimization Framework ◽  
Reconstruction Methods ◽  
Alignment Errors

Three-dimensional (3D) reconstruction methods generate a 3D textured model from the combination of data from several captures. As such, the geometrical transformations between these captures are required. The process of computing or refining these transformations is referred to as alignment. It is often a difficult problem to handle, in particular due to a lack of accuracy in the matching of features. We propose an optimization framework that takes advantage of fiducial markers placed in the scene. Since these markers are robustly detected, the problem of incorrect matching of features is overcome. The proposed procedure is capable of enhancing the 3D models created using consumer level RGB-D hand-held cameras, reducing visual artefacts caused by misalignments. One problem inherent to this solution is that the scene is polluted by the markers. Therefore, a tool was developed to allow their removal from the texture of the scene. Results show that our optimization framework is able to significantly reduce alignment errors between captures, which results in visually appealing reconstructions. Furthermore, the markers used to enhance the alignment are seamlessly removed from the final model texture.

A Fast and Smooth Carving Algorithm for Online 3D Reconstruction

2013 ◽  
Vol 311 ◽  
pp. 153-157
Author(s):  
Xing Gao ◽  
Ning Yu ◽  
Ming Hong Liao
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Heritage Preservation ◽  
Reconstruction Methods ◽  
Space Carving ◽  
Dimensional Reconstruction ◽  
Bio Engineering ◽  
Current Reconstruction

Online rapid three-dimensional reconstruction is widely applied in virtual reality, heritage preservation, bio-engineering and architectural fields. The error caused by image quality or manual import is the main reason for the low quality of model details when applying current reconstruction methods while meeting the time premise. To solve this problem, the paper proposes a fast and smooth carving algorithm for online 3d reconstruction by joining the filter. By applying the method, you can get a more realistic and smooth three-dimensional reconstruction results. First, we convert the input point cloud to meshes through Delaunay tetrahedralisation. Then we reconstruct the model with the space carving algorithm with the filter to obtain the result. The experiment result shows our method exceeds existing methods while meeting the time constraints under the premise at the same time.

scholarly journals A Review of Techniques for 3D Reconstruction of Indoor Environments

2020 ◽  
Vol 9 (5) ◽  
pp. 330 ◽  
Author(s):  
Zhizhong Kang ◽  
Juntao Yang ◽  
Zhou Yang ◽  
Sai Cheng
Keyword(s):  
3D Reconstruction ◽  
Indoor Environment ◽  
Three Dimensional ◽  
Future Research ◽  
Indoor Environments ◽  
Seamless Integration ◽  
Advantages And Disadvantages ◽  
Reconstruction Methods ◽  
Benchmark Datasets ◽  
Pyramid Structures

Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.

scholarly journals Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3458
Author(s):  
Chong Yang ◽  
Fan Zhang ◽  
Yunlong Gao ◽  
Zhu Mao ◽  
Liang Li ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Three Dimensional ◽  
Texture Mapping ◽  
3D Modelling ◽  
Car Detection ◽  
Reconstruction Methods ◽  
Geometric Deformation ◽  
Urban Modelling ◽  
Oblique Images

With the progress of photogrammetry and computer vision technology, three-dimensional (3D) reconstruction using aerial oblique images has been widely applied in urban modelling and smart city applications. However, state-of-the-art image-based automatic 3D reconstruction methods cannot effectively handle the unavoidable geometric deformation and incorrect texture mapping problems caused by moving cars in a city. This paper proposes a method to address this situation and prevent the influence of moving cars on 3D modelling by recognizing moving cars and combining the recognition results with a photogrammetric 3D modelling procedure. Through car detection using a deep learning method and multiview geometry constraints, we can analyse the state of a car’s movement and apply a proper preprocessing method to the geometrically model generation and texture mapping steps of 3D reconstruction pipelines. First, we apply the traditional Mask R-CNN object detection method to detect cars from oblique images. Then, a detected car and its corresponding image patch calculated by the geometry constraints in the other view images are used to identify the moving state of the car. Finally, the geometry and texture information corresponding to the moving car will be processed according to its moving state. Experiments on three different urban datasets demonstrate that the proposed method is effective in recognizing and removing moving cars and can repair the geometric deformation and error texture mapping problems caused by moving cars. In addition, the methods proposed in this paper can be applied to eliminate other moving objects in 3D modelling applications.

scholarly journals A Multi-Perspective 3D Reconstruction Method with Single Perspective Instantaneous Target Attitude Estimation

2019 ◽  
Vol 11 (11) ◽  
pp. 1277
Author(s):  
Dan Xu ◽  
Mengdao Xing ◽  
Xiang-Gen Xia ◽  
Guang-Cai Sun ◽  
Jixiang Fu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Matrix Factorization ◽  
Three Dimensional ◽  
Single Layer ◽  
Factorization Method ◽  
Attitude Estimation ◽  
Reconstruction Method ◽  
Limited Information ◽  
Radar Images ◽  
Reconstruction Methods

Due to the limited information of two-dimensional (2D) radar images, the study of three-dimensional (3D) radar image reconstruction has received significant attention. However, the target attitude obtained by the existing 3D reconstruction methods is unknown. In addition, using a single perspective, one can only get 3D reconstruction result of a simple target. For a complex target, due to occlusion and scattering characteristics, 3D reconstruction information obtained from a single perspective is limited. To tackle the above two problems, this paper proposes a new method for multi-perspective 3D reconstruction and single perspective instantaneous target attitude estimation. This method consists of three steps. First, the result of 3D reconstruction with unknown attitude is obtained by the traditional matrix factorization method. Then, in order to obtain the attitude of a target 3D reconstruction, additional constraints are added to the projection vectors which are computed from the matrix factorization method. Finally, the information from different perspectives are merged into a single layer information according to certain rules. After the information fusion, a multi-perspective 3D reconstruction structure with better visibility and more information is obtained. Simulation results have proved the effectiveness and robustness of the proposed method.

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

Structural Studies on the Eukaryotic Ribosome

1990 ◽  
Vol 48 (1) ◽  
pp. 256-257
Author(s):  
Adriana Verschoor ◽  
Ronald Milligan ◽  
Suman Srivastava ◽  
Joachim Frank
Keyword(s):  
Chick Embryo ◽  
3D Reconstruction ◽  
Single Particle ◽  
Mass Density ◽  
Particle Surface ◽  
Uranyl Acetate ◽  
Electron Microscopic ◽  
Eukaryotic Ribosome ◽  
Negative Stain ◽  
Reconstruction Methods

We have studied the eukaryotic ribosome from two vertebrate species (rabbit reticulocyte and chick embryo ribosomes) in several different electron microscopic preparations (Fig. 1a-d), and we have applied image processing methods to two of the types of images. Reticulocyte ribosomes were examined in both negative stain (0.5% uranyl acetate, in a double-carbon preparation) and frozen hydrated preparation as single-particle specimens. In addition, chick embryo ribosomes in tetrameric and crystalline assemblies in frozen hydrated preparation have been examined. 2D averaging, multivariate statistical analysis, and classification methods have been applied to the negatively stained single-particle micrographs and the frozen hydrated tetramer micrographs to obtain statistically well defined projection images of the ribosome (Fig. 2a,c). 3D reconstruction methods, the random conical reconstruction scheme and weighted back projection, were applied to the negative-stain data, and several closely related reconstructions were obtained. The principal 3D reconstruction (Fig. 2b), which has a resolution of 3.7 nm according to the differential phase residual criterion, can be compared to the images of individual ribosomes in a 2D tetramer average (Fig. 2c) at a similar resolution, and a good agreement of the general morphology and of many of the characteristic features is seen.Both data sets show the ribosome in roughly the same ’view’ or orientation, with respect to the adsorptive surface in the electron microscopic preparation, as judged by the agreement in both the projected form and the distribution of characteristic density features. The negative-stain reconstruction reveals details of the ribosome morphology; the 2D frozen-hydrated average provides projection information on the native mass-density distribution within the structure. The 40S subunit appears to have an elongate core of higher density, while the 60S subunit shows a more complex pattern of dense features, comprising a rather globular core, locally extending close to the particle surface.

3D reconstruction and calculation of surface area and volume of bell pepper

2007 ◽  
Vol 3 (1) ◽  
pp. 89-113
Author(s):  
Zoltán Gillay ◽  
László Fenyvesi
Keyword(s):  
3D Reconstruction ◽  
Surface Area ◽  
Three Dimensional ◽  
Reference Method ◽  
Bell Pepper ◽  
Three Dimensional Model ◽  
Acceptable Error ◽  
Volume Calculation ◽  
Bell Peppers ◽  
Area And Volume

There was a method developed that generates the three-dimensional model of not axisymmetric produce, based on an arbitrary number of photos. The model can serve as a basis for calculating the surface area and the volume of produce. The efficiency of the reconstruction was tested on bell peppers and artificial shapes. In case of bell peppers 3-dimensional reconstruction was created from 4 images rotated in 45° angle intervals. The surface area and the volume were estimated on the basis of the reconstructed area. Furthermore, a new and simple reference method was devised to give precise results for the surface area of bell pepper. The results show that this 3D reconstruction-based surface area and volume calculation method is suitable to determine the surface area and volume of definite bell peppers with an acceptable error.

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 20506-1-20506-7
Author(s):  
Min Zhu ◽  
Rongfu Zhang ◽  
Pei Ma ◽  
Xuedian Zhang ◽  
Qi Guo
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Scale Model ◽  
Microscopic Image ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Cost Aggregation ◽  
Dimensional Reconstruction ◽  
Image Pairs ◽  
Accurate Matching

Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

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