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Segmentation and measurement of the cortex from 3D MR images

  • Xiaolan Zeng
  • Lawrence H. Staib
  • Robert T. Schultz
  • James S. Duncan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

The cortex is the outermost thin layer of gray matter in the brain; geometric measurement of the cortex helps in understanding brain anatomy and function. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various measurements are key steps. While manual segmentation is tedious and labor intensive, automatic, reliable and efficient segmentation and measurement of the cortex remain challenging problems due to its convoluted nature. A new approach of coupled surfaces propagation using level set methods is presented here for the problem of the segmentation and measurement of the cortex. Our method is motivated by the nearly constant thickness of the cortical mantle and takes this tight coupling as an important constraint. By evolving two embedded surfaces simultaneously, each driven by its own image-derived information while maintaining the coupling, a final representation of the cortical bounding surfaces and an automatic segmentation of the cortex are achieved. Characteristics of the cortex such as cortical surface area, surface curvature and thickness are then evaluated. The level set implementation of surface propagation offers the advantage of easy initialization, computational efficiency and the ability to capture deep folds of the sulci. Results and validation from various experiments on simulated and real 3D MR images are provided.

Keywords

Gray Matter Frontal Lobe Gray Matter Volume Cortical Surface Shape Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Xiaolan Zeng
    • 1
  • Lawrence H. Staib
    • 1
  • Robert T. Schultz
    • 2
  • James S. Duncan
    • 1
  1. 1.Departments of Electrical Engineering and Diagnostic RadiologyYale UniversityNew Haven
  2. 2.Child Study CenterYale UniversityNew Haven

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