Automatic Virus Particle Selection-The Entropy Approach

This paper describes a fully automatic approach to locate icosahedral virus particles in transmission electron microscopy images. The initial detection of the particles takes place through automatic segmentation of the entropy-proportion image; this image is computed in particular regions of interes...

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Vydané v:IEEE transactions on image processing Ročník 22; číslo 5; s. 1996 - 2003
Hlavní autori: Proenca, M. D. C. M. S., Nunes, J. F. M., de Matos, A. P. A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.05.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adenoviridae
Adenovirus
Adenoviruses
Algorithm design and analysis
Algorithms
Applied sciences
automatic detection
automatic particle selection
Detection, estimation, filtering, equalization, prediction
electron microscopy images
Entropy
Exact sciences and technology
Humans
icosahedral particles
Image processing
Image Processing, Computer-Assisted - methods
Information, signal and communications theory
Microscopy
Microscopy, Electron, Transmission
Pattern Recognition, Automated - methods
Reproducibility of Results
segmentation
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Virion - isolation & purification
Virion - ultrastructure
Segmentation
Adenovirus
Automatic selection
Entropy
Electron microscopy
Texture
Discrimination task
Outlier
Interest region
Computer virus
False negative
Automatic measurement
icosahedral particles
Signal processing
Feature extraction
automatic particle selection
automatic detection
electron microscopy images
Computer security
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:This paper describes a fully automatic approach to locate icosahedral virus particles in transmission electron microscopy images. The initial detection of the particles takes place through automatic segmentation of the entropy-proportion image; this image is computed in particular regions of interest defined by two concentric structuring elements contained in a small overlapping window running over all the image. Morphological features help to select the candidates, as the threshold is kept low enough to avoid false negatives. The candidate points are subject to a credibility test based on features extracted from eight radial intensity profiles in each point from a texture image. A candidate is accepted if these features meet the set of acceptance conditions describing the typical intensity profiles of these kinds of particles. The set of points accepted is subjected to a last validation in a three-parameter space using a discrimination plan that is a function of the input image to separate possible outliers.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2013.2244216