Discriminative cue integration for medical image annotation

Tatiana Tommasi, Francesco Orabona, Barbara Caputo

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a multi-cue approach to automatic medical image annotation. We represent images using global and local features. These cues are then combined using three alternative approaches, all based on the support vector machine algorithm. We tested our methods on the IRMA database, and with two of the three approaches proposed here we participated in the 2007 ImageCLEFmed benchmark evaluation, in the medical image annotation track. These algorithms ranked first and fifth, respectively among all submission. Experiments using the third approach also confirm the power of cue integration for this task. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish (US)
Pages (from-to)1996-2002
Number of pages7
JournalPattern Recognition Letters
Issue number15
StatePublished - Nov 1 2008
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Software
  • Computer Vision and Pattern Recognition


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