Abstract
The extraction of brain tissue from brain MRI images is an important pre-procedure for the neuroimaging analyses. The brain is bilaterally symmetric both in coronal plane and transverse plane, but is usually asymmetric in sagittal plane. To address the over-smoothness, boundary leakage, local convergence and asymmetry problems in many popular methods, we developed a brain extraction method using an active contour neighborhood-based graph cuts model. The method defined a new asymmetric assignment of edge weights in graph cuts for brain MRI images. The new graph cuts model was performed iteratively in the neighborhood of brain boundary named the active contour neighborhood (ACN), and was effective to eliminate boundary leakage and avoid local convergence. The method was compared with other popular methods on the Internet Brain Segmentation Repository (IBSR) and OASIS data sets. In testing cross IBSR data set (18 scans with 1.5 mm thickness), IBSR data set (20 scans with 3.1 mm thickness) and OASIS data set (77 scans with 1 mm thickness), the mean Dice similarity coefficients obtained by the proposed method were 0.957 ± 0.013, 0.960 ± 0.009 and 0.936 ± 0.018 respectively. The result obtained by the proposed method is very similar with manual segmentation and achieved the best mean Dice similarity coefficient on IBSR data. Our experiments indicate that the proposed method can provide competitively accurate results and may obtain brain tissues with sharp brain boundary from brain MRI images.
Highlights
Brain extraction or skull stripping is needed before most of neuroimaging analyses, such as registration between MRI images [1,2,3], measurement of brain volume [4,5], brain tissue classification [6], and cortical surface reconstruction [7]
The above methods have greatly improved the accuracy and robustness for brain extraction, these methods can not completely substitute for manual method for the appearances of over-smoothness, leakage through a weak boundary and missing brain tissues caused by local convergence
The tissues and non-brain considered the Object defined graph cuts modelbrain for brain extraction, the edge tissues weightsare between all theas vertices
Summary
Brain extraction or skull stripping is needed before most of neuroimaging analyses, such as registration between MRI images [1,2,3], measurement of brain volume [4,5], brain tissue classification [6], and cortical surface reconstruction [7]. Some hybrid-based methods warp the brain volume to an atlas using registration techniques before brain extraction, use the parameter learning techniques such as meta-algorithm, random forest and neural networks to get the proper initial region or parameters to perform brain extraction. It is effective that we use local region features to obtain sharp brain boundary and eliminate the leakage, but it is easy to lead to local convergence at the edges between the white matter and gray matter if the initial region is far from the true brain boundary Trying to address these problems, we proposed a new brain extraction method from T1-weighted MRI volume.
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