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Segmentation in dermatological hyperspectral images: dedicated methods

Overview of attention for article published in BioMedical Engineering OnLine, August 2016
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Title
Segmentation in dermatological hyperspectral images: dedicated methods
Published in
BioMedical Engineering OnLine, August 2016
DOI 10.1186/s12938-016-0219-5
Pubmed ID
Authors

Robert Koprowski, Paweł Olczyk

Abstract

Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis. The segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm. As a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE-2.3 ms, S3D-1949 ms, SH-844 ms for the computer with Intel(®) Core i7 4960X CPU 3.6 GHz) and more accurate (SKE-accuracy 79 %, S3D-90 %, SH-92 %) in comparison with typical methods known from the literature. Profiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Master 2 17%
Student > Doctoral Student 2 17%
Lecturer 1 8%
Unknown 4 33%
Readers by discipline Count As %
Engineering 2 17%
Computer Science 2 17%
Mathematics 1 8%
Medicine and Dentistry 1 8%
Energy 1 8%
Other 0 0%
Unknown 5 42%
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 18 August 2016.
All research outputs
#18,467,727
of 22,883,326 outputs
Outputs from BioMedical Engineering OnLine
#564
of 822 outputs
Outputs of similar age
#262,304
of 342,741 outputs
Outputs of similar age from BioMedical Engineering OnLine
#12
of 13 outputs
Altmetric has tracked 22,883,326 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.