Three-Dimensional Analysis of Biological Specimens Utilizing Image Processing Techniques

1980 ◽  
Author(s):  
David A. Agard ◽  
John W. Sedat
Keyword(s):  
Image Processing ◽  
Dimensional Analysis ◽  
Three Dimensional ◽  
Image Processing Techniques ◽  
Processing Techniques

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

scholarly journals Improved Visualisation of Patient-Specific Heart Structure Using Three-Dimensional Printing Coupled with Image-Processing Techniques Inspired by Astrophysical Methods

2019 ◽  
Vol 9 (2) ◽  
pp. 267-273
Author(s):  
I. Brewis ◽  
J. A. Mclaughlin
Keyword(s):  
Image Processing ◽  
Cardiac Computed Tomography ◽  
Three Dimensional ◽  
Heart Tissue ◽  
Patient Specific ◽  
Small Scale ◽  
Image Processing Techniques ◽  
Stl File ◽  
Novel Approach ◽  
Processing Techniques

The aim of this study is to use image-processing techniques developed in the field of astrophysics as inspiration for a novel approach to the three-dimensional (3D) imaging of periprocedural medical data, with the intention of providing improved visualisation of patient-specific heart structure and thereby allowing for an improved quality of procedural planning with regards to individualized cardiovascular healthcare. Using anonymized patient DICOM data for a cardiac computed tomography (CT) angiography, two-dimensional slices of the patient's heart were processed using a series of software packages in order to produce an accurate 3D representation of the patient's heart tissue as a computer-generated stereolithography (STL) file, followed by the creation of a tactile 3D printout. We find that the models produced provide clear definition of heart structure, in particular in the left atrium, left ventricle and aorta. This level of clarity also allows for the aortic valve to be observed and 3D printed. This study provides a step-by-step blueprint of how this can be achieved using open source software, specifically Slicer 4.8.1, MeshLab and AutoDesk Netfabb. In addition, the implementation of astrophysical image-processing techniques shows an improvement in modelling of the heart based on the CT data, in particular in the case of small-scale features where echocardiography has previously been required for more reliable results.

IDENTIFICATION OF COHERENT STRUCTURES IN THREE-DIMENSIONAL SIMULATIONS OF A FLUID-MIX PROBLEM

2009 ◽  
Vol 09 (03) ◽  
pp. 389-410 ◽  
Author(s):  
CHANDRIKA KAMATH ◽  
ABEL GEZAHEGNE ◽  
PAUL MILLER
Keyword(s):  
Image Processing ◽  
Coherent Structures ◽  
High Performance ◽  
Three Dimensional ◽  
Data Sets ◽  
Image Processing Techniques ◽  
Two Fluids ◽  
Split And Merge ◽  
Processing Techniques ◽  
Three Dimensional Simulations

The use of high-performance computers in computer simulations of physical phenomena has resulted in the generation of massive amounts of data. The analysis of these data can be challenging given their size and the complexity of the structures observed. In this paper, we describe how we can use image processing techniques to identify and count coherent structures in three-dimensional simulations of the Rayleigh-Taylor instability, which occurs when one fluid is being accelerated by a second fluid. As the two fluids mix, coherent structures are formed which grow, shrink, change shape, split, and merge with time. We describe the challenges faced in the analysis and show how we can combine image processing techniques with problem-specific characteristics to successfully analyze terabyte-sized data sets.

Digital reconstruction of calcified early metazoans, terminal Proterozoic Nama Group, Namibia

Paleobiology ◽  
2001 ◽  
Vol 27 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Wesley A. Watters ◽  
John P. Grotzinger
Keyword(s):  
Mathematical Model ◽  
Image Processing ◽  
Delaunay Triangulation ◽  
Cross Sections ◽  
Mathematical Description ◽  
Three Dimensional ◽  
Image Processing Techniques ◽  
Digital Reconstruction ◽  
Processing Techniques ◽  
The Mathematical Model

A method is presented for the digital reconstruction of weakly calcified fossils within the Nama Group, Namibia. These recently described fossils (Grotzinger et al. 2000) are preserved as calcitic void-fill in a calcite matrix, and individual specimens cannot be freed by conventional techniques. The technique presented here has several integrated steps: (1) the analysis of cross-sections of fossil specimens, (2) the construction of a three-dimensional “tomographic” model that is assembled from the cross-sections, (3) the development of an idealized mathematical model based upon geometric parameters measured from the tomographic model, and (4) the visualization of randomly oriented cross-sections through the mathematical model, which can be compared with fossil cross-sections in outcrop.In this procedure, rocks containing the fossils are ground and digitally photographed at thickness intervals of 25 μm. A battery of image-processing techniques is used to obtain the contour outlines of the fossils in serial cross-sections. A Delaunay triangulation method is then used to reconstruct the morphology from tetrahedrons which connect the contours in adjacent layers. We found that most of the fossils represent a single morphology with some well-defined characters that vary slightly among individual specimens. This fossil morphology was described by Grotzinger et al. (2000) as Namacalathus hermanastes. A mathematical description of the morphology is used to obtain a database of randomly oriented synthetic cross-sections. This database reproduces the vast majority of cross-sections observed in outcrop.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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