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Visual parameter optimisation for biomedical image processing

Overview of attention for article published in BMC Bioinformatics, August 2015
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1 tweeter

Citations

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19 Mendeley
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Title
Visual parameter optimisation for biomedical image processing
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/1471-2105-16-s11-s9
Pubmed ID
Authors

AJ Pretorius, Y Zhou, RA Ruddle

Abstract

Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 47%
Researcher 2 11%
Lecturer 1 5%
Professor 1 5%
Student > Bachelor 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Computer Science 9 47%
Engineering 5 26%
Neuroscience 1 5%
Earth and Planetary Sciences 1 5%
Unknown 3 16%

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 22 September 2015.
All research outputs
#4,719,659
of 6,386,702 outputs
Outputs from BMC Bioinformatics
#2,614
of 3,108 outputs
Outputs of similar age
#137,998
of 200,600 outputs
Outputs of similar age from BMC Bioinformatics
#131
of 145 outputs
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So far Altmetric has tracked 3,108 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.