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Digital imaging of colon tissue: method for evaluation of inflammation severity by spatial frequency features of the histological images

Overview of attention for article published in Diagnostic Pathology, September 2015
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1 tweeter

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1 Dimensions

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12 Mendeley
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Title
Digital imaging of colon tissue: method for evaluation of inflammation severity by spatial frequency features of the histological images
Published in
Diagnostic Pathology, September 2015
DOI 10.1186/s13000-015-0389-7
Pubmed ID
Authors

Robertas Petrolis, Rima Ramonaitė, Dainius Jančiauskas, Juozas Kupčinskas, Rokas Pečiulis, Limas Kupčinskas, Algimantas Kriščiukaitis

Abstract

The efficacy of histological analysis of colon sections used for evaluation of inflammation severity can be improved by means of digital imaging giving quantitative estimates of main diagnostic features. The aim of this study was to reveal most valuable diagnostic features reflecting inflammation severity in colon and elaborate the evaluation method for computer-aided diagnostics. Tissue specimens from 24 BALB/c mice and 15 patients were included in the study. Chronic and acute colon inflammation in mice was induced by oral administration of dextran sulphate sodium (DSS) solution, while mice in the control group did not get DSS. Human samples of inflamed colon tissue were obtained from patients with ulcerative colitis (n = 6). Non-inflamed colon tissue of control subjects (n = 9) was obtained from patients with irritable bowel syndrome or functional obstipation. Analysis of morphological changes in mice and human colon mucosa was performed using 4-μm haematoxylin-eosin (HE) sections. The features reflecting morphological changes in the images of colon mucosa were calculated by convolution of Gabor filter bank and array of pixel values. All features were generalized by calculating mean, histogram skewness and entropy of every image response. Principal component analysis was used to construct optimal representation of morphological changes. First principal component (PC1) was representing the major part of features variation (97 % in mice and 71 % in human specimens) and was selected as a measure of inflammation severity. Validation of new measure was performed by means of custom-made software realizing double blind comparison of differences in PC1 with expert's opinion about inflammation severity presented in two compared pictures. Overall accuracy of 80 % for mice and 67 % for human was reached. Principal component analysis of spatial frequency features of histological images may provide continuous scale estimation of inflammation severity of colon tissue.

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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Bachelor 2 17%
Student > Ph. D. Student 1 8%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Other 2 17%
Unknown 2 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 25%
Medicine and Dentistry 2 17%
Immunology and Microbiology 1 8%
Computer Science 1 8%
Decision Sciences 1 8%
Other 2 17%
Unknown 2 17%

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 15 September 2015.
All research outputs
#2,992,496
of 6,344,720 outputs
Outputs from Diagnostic Pathology
#295
of 632 outputs
Outputs of similar age
#106,083
of 196,486 outputs
Outputs of similar age from Diagnostic Pathology
#32
of 50 outputs
Altmetric has tracked 6,344,720 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 632 research outputs from this source. They receive a mean Attention Score of 1.9. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 196,486 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.