Multivariate statistical model for 3D image segmentation with application to medical images

Nigel M. John, Mansur R. Kabuka, Mohamed O. Ibrahim

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations


In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Original languageEnglish (US)
Pages (from-to)365-377
Number of pages13
JournalJournal of Digital Imaging
Issue number4
StatePublished - Dec 2003


  • 3D
  • Brain
  • Magnetic resonance imaging (MRI)
  • Segmentation
  • Statistical

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Computer Vision and Pattern Recognition


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