Volumetric segmentation of magnetic resonance images

James L. Lee, Jeffrey J. Rodriguez

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

1 Scopus citations


Current computer graphics techniques can generate 3-D views of the human anatomy from magnetic resonance images. These techniques require that the images first be segmented into the various tissue types. However, there has been no fully automated system that can perform this task on a single set of high-resolution 3-D magnetic resonance images. We present a fully automated segmentation algorithm based on the 3-D difference of Gaussians (DOG) filter. A novel method for the classification of regions found by the DOG filter, as well as a correction procedure that detects errors from the DOG filter, is presented. Regions are classified based on the mean gray level of the voxels within closed contours. In previous work, the user had to manually split falsely merged regions. Our automated correction algorithm detects such errors and splits the merged regions. Spatial information is also incorporated to help discriminate between tissues. Encouraging results were obtained with an average of less than five percent error in each image. Integral shading is used to obtain a 3-D rendering of the data set.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsAndrew G. Tescher
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages10
ISBN (Print)0819416223
StatePublished - 1994
EventApplications of Digital Image Processing XVII - San Diego, CA, USA
Duration: Jul 26 1994Jul 29 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherApplications of Digital Image Processing XVII
CitySan Diego, CA, USA

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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