Superpixel-based automatic segmentation of villi in confocal endomicroscopy

D. Boschetto, H. Mirzaei, R. W L Leong, E. Grisan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Place of PublicationNew York
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages168-171
Number of pages4
ISBN (Electronic)9781509024551
DOIs
Publication statusPublished - 18 Apr 2016
Externally publishedYes
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: 24 Feb 201627 Feb 2016

Other

Other3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
CountryUnited States
CityLas Vegas
Period24/02/1627/02/16

Keywords

  • automatic identification
  • classification
  • confocal endomicroscopy
  • segmentation
  • SLIC superpixel

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