Magnetic resonance image tissue classification using an automatic method

Diagnostic Pathology

Alireza Karimian¹  Amirhosein Riazi²  Rubiyah Yusof³  Sepideh Yazdani³ 

[1] Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran;Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran;Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan semarak, Kuala Lumpur 54100, Malaysia

关键词: Brain tissue classification;    SVMs;    Histogram-based segmentation method;    Image segmentation;    Magnetic resonance imaging;    Statistical segmentation;   

Others  :  1175189
DOI  :  10.1186/s13000-014-0207-7

 received in 2014-08-22, accepted in 2014-10-08,  发布年份 2014

【 摘 要 】

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.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_207 webcite

【 授权许可】

   
2014 Yazdani et al.; licensee BioMed Central.

【 图 表 】

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