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Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image

Overview of attention for article published in BMC Bioinformatics, September 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

twitter
8 tweeters
facebook
1 Facebook page

Citations

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35 Dimensions

Readers on

mendeley
26 Mendeley
citeulike
1 CiteULike
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Title
Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0739-1
Pubmed ID
Authors

Tsuyoshi Kato, Raissa Relator, Hayliang Ngouv, Yoshihiro Hirohashi, Osamu Takaki, Tetsuhiro Kakimoto, Kinya Okada

Abstract

The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection. A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG. The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 31%
Student > Master 4 15%
Student > Ph. D. Student 3 12%
Unspecified 2 8%
Professor > Associate Professor 2 8%
Other 4 15%
Unknown 3 12%
Readers by discipline Count As %
Medicine and Dentistry 9 35%
Computer Science 6 23%
Engineering 3 12%
Unspecified 2 8%
Immunology and Microbiology 1 4%
Other 2 8%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 08 January 2016.
All research outputs
#4,055,019
of 14,557,796 outputs
Outputs from BMC Bioinformatics
#1,776
of 5,415 outputs
Outputs of similar age
#68,159
of 249,587 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 2 outputs
Altmetric has tracked 14,557,796 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,415 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% of its peers.
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 249,587 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them