Fuzzy-connected 3D image segmentation at interactive speeds

Laszlo G. Nyul, Alexandre X. Falcao, Jayaram K. Udupa

Research output: Contribution to journalConference article

11 Citations (Scopus)


Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 algorithms under two categories - label correcting and label setting. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-360 fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue specific meaning (such as CT or standardized MR images) are utilized, all parameters for the segmentation method can be fixed once for all, all intermediate data can be computed before the user interaction is needed, and the user can be provided with more information at the time of interaction.

Original languageEnglish
Pages (from-to)I/-
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - Jan 1 2000
EventMedical Imaging 2000: Image Processing - San Diego, CA, USA
Duration: Feb 14 2000Feb 17 2000

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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