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Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions

Overview of attention for article published in Sensors, December 2017
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Title
Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions
Published in
Sensors, December 2017
DOI 10.3390/s17122823
Pubmed ID
Authors

Declan O’Loughlin, Bárbara L. Oliveira, Muhammad Adnan Elahi, Martin Glavin, Edward Jones, Milica Popović, Martin O’Halloran

Abstract

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 25%
Lecturer > Senior Lecturer 3 13%
Researcher 3 13%
Student > Bachelor 2 8%
Student > Master 2 8%
Other 2 8%
Unknown 6 25%
Readers by discipline Count As %
Engineering 12 50%
Computer Science 1 4%
Agricultural and Biological Sciences 1 4%
Neuroscience 1 4%
Physics and Astronomy 1 4%
Other 0 0%
Unknown 8 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2018.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from Sensors
#16,643
of 24,312 outputs
Outputs of similar age
#385,559
of 446,025 outputs
Outputs of similar age from Sensors
#253
of 322 outputs
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