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Gradient Competition Anisotropy for Centerline Extraction and Segmentation of Spinal Cords

  • Max W. K. Law
  • Gregory J. Garvin
  • Sudhakar Tummala
  • KengYeow Tay
  • Andrew E. Leung
  • Shuo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Centerline extraction and segmentation of the spinal cord – an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is threefold – 1) The gradient competition descriptor compares the image gradient obtained at different detection scales to suppress neighboring disturbance. It reliably recognizes the curvilinearity and orientations of elliptical curvilinear objects. 2) The orientation coherence anisotropy analyzes the detection responses offered by the gradient competition descriptor. It enforces structure orientation consistency to sustain strong disturbance introduced by high contrast neighboring objects to perform centerline extraction. 3) The intensity coherence segmentation quantifies the intensity difference between the centerline and the voxels in the vicinity of the centerline. It effectively removes the object intensity variation along the structure to accurately delineate the target structure. They constitute the gradient competition anisotropy method which can robustly and accurately detect the centerline and boundary of the spinal cord. It is validated and compared using 25 clinical datasets. It is demonstrated that the proposed method well suits the applications of spinal cord centerline extraction and segmentation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Max W. K. Law
    • 1
    • 2
  • Gregory J. Garvin
    • 2
    • 4
  • Sudhakar Tummala
    • 2
  • KengYeow Tay
    • 2
    • 3
  • Andrew E. Leung
    • 2
    • 3
  • Shuo Li
    • 1
    • 2
  1. 1.GE HealthcareCanada
  2. 2.University of Western OntarioLondonCanada
  3. 3.London Health Sciences CentreLondonCanada
  4. 4.St. Joseph’s Health Care LondonLondonCanada

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