Publication: Random forests for dura mater microvasculature segmentation using epifluorescence images
Issued Date
2016-10-13
Resource Type
ISSN
1557170X
Other identifier(s)
2-s2.0-85009083425
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol.2016-October, (2016), 2901-2904
Suggested Citation
Yasmin M. Kassim, V. B.Surya Prasath, Rengarajan Pelapur, Olga V. Glinskii, Richard J. Maude, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan Random forests for dura mater microvasculature segmentation using epifluorescence images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol.2016-October, (2016), 2901-2904. doi:10.1109/EMBC.2016.7591336 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43433
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Title
Random forests for dura mater microvasculature segmentation using epifluorescence images
Abstract
© 2016 IEEE. 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 structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.