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Magnetic resonance image tissue classification using an automatic method

Yazdani S 1 , Yusof R 2 , Riazi A 3 , Karimian A 4

Affiliations 

  • 1 Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan semarak, Kuala Lumpur, 54100, Malaysia. ysepideh2@live.utm.my
  • 2 Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan semarak, Kuala Lumpur, 54100, Malaysia. rubiyah.kl@utm.my
  • 3 Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran. amirh.riazi@gmail.com
  • 4 Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. karimian@eng.ui.ac.ir
Diagn Pathol, 2014;9:207.
PMID: 25540017 DOI: 10.1186/s13000-014-0207-7

Abstract

Brain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex).

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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