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Adaptive template moderated spatially varying statistical classification

  • Simon K. Warfield
  • Michael Kaus
  • Ferenc A. Jolesz
  • Ron Kikinis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy. The new algorithm is a form of spatially varying classification (SVC), in which an explicit anatomical template is used to moderate the segmentation obtained by k Nearest Neighbour (k-NN) statistical classification. The new algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which creates an adaptive, template moderated (ATM), spatially varying classification (SVC).

The ATM SVC algorithm was applied to several segmentation problems, involving different types of imaging and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of babies, MRI of knee cartilage of normal volunteers) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumours, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone.

Keywords

template moderated segmentation elastic matching nearest neighbour classification knee cartilage neonate brain tumour 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Simon K. Warfield
    • 1
  • Michael Kaus
    • 1
  • Ferenc A. Jolesz
    • 1
  • Ron Kikinis
    • 1
  1. 1.Surgical Planning Laboratory, Harvard Medical School, Department of RadiologyBrigham and Women’s HospitalBoston

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