Superpixel-based automatic segmentation of villi in confocal endomicroscopy

Conference paper


Boschetto, D, Mirzaei, H, Leong, RWL and Grisan, E (2016). Superpixel-based automatic segmentation of villi in confocal endomicroscopy. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Las Vegas, NV, USA 24 - 27 Feb 2016 pp. 168-171 doi:10.1109/BHI.2016.7455861
AuthorsBoschetto, D, Mirzaei, H, Leong, RWL and Grisan, E
TypeConference paper
Abstract

Confocal Laser Endomicroscopy (CLE) is a technique permitting on-site microscopy of the gastrointestinal mucosa after the application of a fluorescent agent, allowing the evaluation of mucosa alterations. These are used as features by skilled technicians to stage the severity of multiple diseases, celiac disease or irritable bowel syndrome among the others. We present an automatic method for villi detection from confocal endoscopy images, whose appearance changes with mucosal alterations. Superpixel segmentation, a well-known technique originating from computer vision, is used to identify and cluster together pixels belonging to uniform regions. Each image in the dataset is analyzed in a multiscale fashion (scale 1, 0.5 and 0.25). From each superpixel, 37 features are extracted at multiple image scales. Each superpixel is classified using a random forest, and a post-processing step is performed to refine the final output. Results in the test set (70 images, 30870 superpixels) show 85.87% accuracy, 92.88% sensitivity, 76.99% specificity in the superpixel space, and 86.36% of accuracy and 87.44% Dice score in the pixel domain. © 2016 IEEE.

Year2016
ISSN2168-2208
Digital Object Identifier (DOI)doi:10.1109/BHI.2016.7455861
Web address (URL)https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968593262&doi=10.1109%2fBHI.2016.7455861&partnerID=40&md5=4b0aab0ac290131f1977491c895492c7
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
Publication dates
Print21 Apr 2016
Publication process dates
Accepted18 Jan 2015
Deposited27 Jan 2020
ISBN978-1-5090-2455-1
Page range168-171
Permalink -

https://openresearch.lsbu.ac.uk/item/88xvq

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