A hybrid strategy to integrate surface-based and mutual-information-based methods for co-registering brain SPECT and MR images

Yuan Lin Liao, Yung-Nien Sun, Wan Yuo Guo, Yuan Hwa Chou, Jen Chuen Hsieh, Yu Te Wu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Co-registration of brain SPECT and MR images has been used extensively in clinical applications. The complementary features of two major co-registration methods-surface- and mutual-information-based (MI-based)-motivated us to study a hybrid-based scheme that uses the surface-based method to achieve a quick alignment, followed by the MI-based method for fine tuning. Computer simulations were conducted to evaluate the accuracy and robustness of surface-, MI-, and hybrid-based registration methods by designing different levels of noise and mismatch in the registration experiments. Results demonstrated that the hybrid surface-MI-based scheme outperforms both the surface- and MI-based methods in providing superior accuracy and success rates. Specifically, the translational and rotational errors were no more than 1 mm and 2°, respectively, with consistent success rates over 98%. Besides, the hybrid-based method saved 12-53% of the computation efforts, compared with using the MI-based method alone. We recommend the use of hybrid-based method when the orientational differences between the floating and reference images exceed 10°.

Original languageEnglish
Pages (from-to)671-685
Number of pages15
JournalMedical and Biological Engineering and Computing
Volume49
Issue number6
DOIs
Publication statusPublished - 2011 Jun 1

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'A hybrid strategy to integrate surface-based and mutual-information-based methods for co-registering brain SPECT and MR images'. Together they form a unique fingerprint.

  • Cite this