title: Magnetic resonance image tissue classification using an automatic method.
abstract: BACKGROUND
  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).
  
  
  METHODS
  In this paper we present a simple and more accurate automated technique for brain segmentation into White Matter, Gray Matter and Cerebrospinal fluid (CSF) in three-dimensional MR images. The algorithm's three steps are histogram based segmentation, feature extraction and final classification using SVM. The integrated algorithm has more accurate results than what can be obtained with its individual components. To produce much more efficient segmentation method our framework captures different types of features in each step that are of special importance for MRI, i.e., distributions of tissue intensities, textural features, and relationship with neighboring voxels or spatial features.
  
  
  RESULTS
  Our method has been validated on real images and simulated data, with desirable performance in the presence of noise and intensity inhomogeneities.
  
  
  CONCLUSIONS
  The experimental results demonstrate that our proposed method is a simple and accurate technique to define brain tissues with high reproducibility in comparison with other techniques.
  The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_207.
authors.affiliation: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan semarak, Kuala Lumpur, 54100, Malaysia. ysepideh2@live.utm.my.
authors.forename: Sepideh
authors.initials: S
authors.lastname: Yazdani
authors.affiliation: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan semarak, Kuala Lumpur, 54100, Malaysia. rubiyah.kl@utm.my.
authors.forename: Rubiyah
authors.initials: R
authors.lastname: Yusof
authors.affiliation: 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.
authors.forename: Amirhosein
authors.initials: A
authors.lastname: Riazi
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. karimian@eng.ui.ac.ir.
authors.forename: Alireza
authors.initials: A
authors.lastname: Karimian
mesh_terms: D000465:Algorithms; D057205:Automation, Laboratory; D002555:Cerebrospinal Fluid; D003198:Computer Simulation; D066128:Gray Matter / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D060388:Support Vector Machine; D066127:White Matter / Q000473:pathology*