Algorithms for Extraction of Nanowire Lengths and Positions From Optical Section Microscopy Image Sequence

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Tao Peng ◽  
Arvind Balijepalli ◽  
Satyandra K. Gupta ◽  
Thomas W. LeBrun
Keyword(s):  
Image Processing ◽  
Optical Tweezers ◽  
Image Sequence ◽  
Optical Section ◽  
Image Frame ◽  
Image Processing Techniques ◽  
Microscopy Image ◽  
Processing Techniques ◽  
Areas Of Interest ◽  
Microscopy Images

This paper presents algorithms for estimating length, location, and orientation of nanowires in a fluidic workspace using images obtained by optical section microscopy. Images containing multiple nanowires are first segmented to locate general areas of interest, which are then analyzed to determine discrete nanowire parameters. We use a set of image processing techniques to extract features of nanowire image patterns, e.g., boundary of nanowire, linear edges, and the intensity profile of nanowire’s diffraction fringes. The parameters of the features are then used to estimate length, 3D position, and 3D orientation of nanowires. A scene representing the workspace is reconstructed using the estimated attributes of nanowires, and it is constantly updated upon the capture of every image frame. We believe that the work described in this paper will be useful for assembly of nanowires using optical tweezers.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

scholarly journals Automated manipulation of non-spherical micro-objects using optical tweezers combined with image processing techniques

2008 ◽  
Vol 16 (19) ◽  
pp. 15115 ◽  
Author(s):  
Yoshio Tanaka ◽  
Hiroyuki Kawada ◽  
Ken Hirano ◽  
Mitsuru Ishikawa ◽  
Hiroyuki Kitajima
Keyword(s):  
Image Processing ◽  
Optical Tweezers ◽  
Image Processing Techniques ◽  
Processing Techniques

Algorithms for Extraction of Nanowires Attributes From Optical Section Microscopy Images

2007 ◽  
Author(s):  
Tao Peng ◽  
Arvind Balijepalli ◽  
Satyandra K. Gupta ◽  
Thomas W. LeBrun
Keyword(s):  
Optical Tweezers ◽  
Building Blocks ◽  
Optical Section ◽  
Extraction Techniques ◽  
Automated Assembly ◽  
Assembly Cell ◽  
Cell Components ◽  
Gradient Based ◽  
Areas Of Interest ◽  
Microscopy Images

Optical tweezers have emerged as a unique tool for micro and nanomanipulation. In an optical tweezers-based assembly cell, components are usually suspended in a fluidic medium and undergo constant random Brownian motion. Automated assembly using optical tweezers requires online monitoring of components in the assembly workspace. Nanowires are very important building blocks for constructing nanoscale devices. This paper presents algorithms for estimating length, location, and orientation of nanowires in the workspace using images obtained by optical section microscopy. The images are first segmented to locate general areas of interest which are then analyzed to determine discrete nanowire parameters. We use image gradient based techniques as well as feature extraction techniques to identify parameters of nanowire image patterns. These parameters are then used to estimate length, location, and orientation of nanowires.

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.

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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