A new pivoting and iterative text detection algorithm for biomedical images

Songhua Xu, Michael Krauthammer

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.

Original languageEnglish (US)
Pages (from-to)924-931
Number of pages8
JournalJournal of Biomedical Informatics
Volume43
Issue number6
DOIs
StatePublished - Dec 2010

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Keywords

  • Biomedical image mining
  • Histogram analysis for text detection
  • Pivoting and iterative text region detection
  • Text detection

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