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Random forests for dura mater microvasculature segmentation using epifluorescence images

Abstract:

Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structure...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/embc.2016.7591336

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Tropical Medicine
Role:
Author
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Grant:
NationalCancerInstitute#R01CA160461(VVG)
R33EB00573(KP
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Grant:
Biomedical Laboratory Research & Development Service #1I01BX000609 (VVG
Publisher:
IEEE Publisher's website
Host title:
2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Journal:
2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) Journal website
Volume:
2016
Pages:
2901-2904
Publication date:
2016-10-18
Acceptance date:
2016-04-22
Event location:
United States
DOI:
EISSN:
1558-4615
ISSN:
1557-170X
Pmid:
28261007
ISBN:
9781457702204
Keywords:
Pubs id:
pubs:672956
UUID:
uuid:68cc61c6-4115-4e4b-9a06-0a47697bc5f0
Local pid:
pubs:672956
Source identifiers:
672956
Deposit date:
2017-08-23

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