Skull Stripping for MRI Images Using Morphological Operators
One of the most common MRI (Magnetic Resonance Imaging) use is a brain visualisation. Brain anatomy is highly complicated therefore it might be difficult to extract only these structures which have diagnostic value. In a consequence it is so necessary to
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Introduction
Skull stripping is a process of the brain tissue segmentation (cortex and cerebellum) from surrounding region (skull and nonbrain area). It is also a very important preprocessing step which precedes further analysis in case of many MRI neurological images (such as image registration or tissue classification) [7]. In clinical practice, this method is widely used in developing automated methods for progress evaluation of neurodegenerative diseases such as: Alzheimer’s disease, multiple sclerosis and other neurological disorders like brain aging or even some mental illness. In search of the most effective skull stripping techniques there were proposed many innovative automatic and semiautomatic brain segmentation techniques c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved K. Saeed and W. Homenda (Eds.): CISIM 2016, LNCS 9842, pp. 172–182, 2016. DOI: 10.1007/978-3-319-45378-1 16
Skull Stripping for MRI Images Using Morphological Operators
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[1–3,6], particularly in the filed of MR image processing. However, published studies comparing common used algorithms [5] demonstrate that each of methodologies has significant number of advantages and disadvantages as well, so it’s very difficult to find one approach which could be consider as the ideal solution. These factors result in increasing need for the further development and examination of new effective skull stripping methods. In this paper one of approaches will be introduced.
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Proposed Method
All images used and processed in this paper are the private property of the Author. They were registered in the John Paul II Hospital in Cracow. The images were registered with the following acquisition parameters: magnetic field: 3T, Spin Echo sequence, T1-weighted, slice thickness: 3 mm and contrast application). During selection of images for algorithms evaluation, the key criteria was to ensure their representativeness (to choose the most common type of images, which are also most often processed and compared in scientific dissertations). Algorithm was tested on images in all 3 projections, all scans were acquired in the middle of the T1-weighted session so that the area of the brain was possibly the greatest in in any case. Skull stripping techniques can be divided into 3 main categories [7]: – Intensity-Based methods. They are based on the threshold classification. The main disadvantage of this approach is its significant sensitivity for intensity fluctuations (in case of MRI caused for example by magnetic field inhomogeneity, registered noise or even device’s properties) – Morphology-based Methods. The basic idea is to combine the use of morphological operations, thresholding and edge detection techniques, in order to separate the area of the brain from the surrounding tissue in the most precisely way. – Deformable Model-based Methods which applied the active contour deformation and fitting to localise brain area and its identification by using image characteristic.
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