Boosting hand-crafted features for curvilinear structure segmentation by learning context filters

Roberto Annunziata, Ahmad Kheirkhah, Pedram Hamrah, Emanuele Trucco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Combining hand-crafted features and learned filters (i.e. feature boosting) for curvilinear structure segmentation has been proposed recently to capture key structure configurations while limiting the number of learned filters. Here, we present a novel combination method pairing hand-crafted appearance features with learned context filters. Unlike recent solutions based only on appearance filters, our method introduces context information in the filter learning process. Moreover, it reduces the potential redundancy of learned appearance filters that may be reconstructed using a combination of hand-crafted filters. Finally, the use of k-means for filter learning makes it fast and easily adaptable to other datasets, even when large dictionary sizes (e.g. 200 filters) are needed to improve performance. Comprehensive experimental results using 3 challenging datasets show that our combination method outperforms recent state-of-the-art HCFs and a recent combination approach for both performance and computational time.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer Verlag
Pages596-603
Number of pages8
ISBN (Print)9783319245737
DOIs
StatePublished - Jan 1 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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