Abstract
BackgroundThe extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model.MethodsThe method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.ResultsThe results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.ConclusionsThe proposed method can provide automated and accurate brain extraction result with high efficiency.
Highlights
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data
Brain extraction which segments magnetic resonance (MR) head images into brain and non-brain region is often required for analyses of neuroimage data
As a pre-processing step, brain extraction is usually performed before a full segmentation of the brain region into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), so that the segmentation problem can be simplified [4,5]
Summary
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. Brain extraction which segments magnetic resonance (MR) head images into brain and non-brain region is often required for analyses of neuroimage data. Compared to two different manual strip-masks, McStrip outperformed BET, SPM and BSE based on the Correct Boundary and Pertinent Boundary criteria and misclassified the least number of brain voxels. These popular methods have both their advantages and weaknesses, and none of them can be accurate and robust enough for large-scale neuroimage analysis [7,8]. We first proposed a new method satisfying the requirement of both fully automated brain extraction and accurate brain extraction result; we summarized the experimental results, evaluation, and comparison of our method to BET and MLS; we discussed the advantages and disadvantages of our method for brain MR image extraction
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