A robust segmentation framework for Spine Trauma diagnosis

Poay Hoon Lim*, Ulas Bagci, Li Bai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Accurate three-dimensional (3D) image segmentation techniques have become increasingly important for medical image analysis in general, and for spinal vertebrae image analysis in particular. The complexity of vertebrae shapes, gaps in the cortical bone and internal boundaries pose significant challenge for image analysis. In this paper, we describe a level set image segmentation framework that integrates prior shape knowledge and local geometrical features to segment both normal and fractured spinal vertebrae. The prior shape knowledge is computed via kernel density estimation whereas the local geometrical features is captured through an edge-mounted Willmore energy. While the shape prior energy draws the level set function towards possible shape boundaries, the Willmore energy helps to capture the detail shape and curvature information of the vertebrae. Experiment on CT images of normal and fractured spinal vertebrae demonstrate promising results in 3D segmentation.

Original languageEnglish (US)
Pages (from-to)25-33
Number of pages9
JournalLecture Notes in Computational Vision and Biomechanics
Volume17
DOIs
StatePublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A robust segmentation framework for Spine Trauma diagnosis'. Together they form a unique fingerprint.

Cite this