An algorithm for automatic segmentation and classification of magnetic resonance brain images

Bradley J. Erickson, Ramesh T.V. Avula

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images. Copyright ® 1998 by W.B. Saunders Company.

Original languageEnglish (US)
Pages (from-to)74-82
Number of pages9
JournalJournal of Digital Imaging
Volume11
Issue number2
DOIs
StatePublished - May 1998

Keywords

  • Automatic multiparametric classification
  • Brain segmentation
  • Magnetic resonance imaging (MRI)
  • Multiple sclerosis (MS)

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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