Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function

  • Shuo Li
  • Thomas Fevens
  • Adam Krzyżak
  • Chao Jin
  • Song Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


This paper proposes a novel method for clinical triple-region image segmentation using a single level set function. Triple-region image segmentation finds wide application in the computer aided X-ray, CT, MRI and ultrasound image analysis and diagnosis. Usually multiple level set functions are used consecutively or simultaneously to segment triple-region medical images. These approaches are either time consuming or suffer from the convergence problems. With the new proposed triple-regions level set energy modelling, the triple-region segmentation is handled within the two region level set framework where only one single level set function needed. Since only a single level set function is used, the segmentation is much faster and more robust than using multiple level set functions. Adapted to the clinical setting, individual principal component analysis and a support vector machine classifier based clinical acceleration scheme are used to accelerate the segmentation. The clinical acceleration scheme takes the strengths of both machine learning and the level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both synthesized and practical images are used to test the proposed method. These results show that the proposed method is able to successfully segment the triple-region using a single level set function. Also this segmentation is very robust to the placement of initial contour. While still quickly converging to the final image, with the clinical acceleration scheme, our proposed method can be used during pre-processing for automatic computer aided diagnosis and surgery.


Support Vector Machine Image Segmentation Initial Contour Medical Image Segmentation Geodesic Active Contour 
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 2006

Authors and Affiliations

  • Shuo Li
    • 1
    • 2
  • Thomas Fevens
    • 2
  • Adam Krzyżak
    • 2
  • Chao Jin
    • 2
  • Song Li
    • 3
  1. 1.GE HealthcareLondonCanada
  2. 2.Department of Computer Science and Software EngineeringConcordia UniversityMontréalCanada
  3. 3.School of StomatologyAnhui Medical UniversityHefeiP.R. China

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