- Open Access
An automated and simple method for brain MR image extraction
© Zhang et al; licensee BioMed Central Ltd. 2011
- Received: 16 June 2011
- Accepted: 13 September 2011
- Published: 13 September 2011
The 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.
The 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.
The 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.
The proposed method can provide automated and accurate brain extraction result with high efficiency.
- Brain Extraction
- Initial Contour
- Intensity Inhomogeneity
- Weak Boundary
- Brain Extraction Tool
Brain extraction which segments magnetic resonance (MR) head images into brain and non-brain region is often required for analyses of neuroimage data. Accurate and automated brain extraction plays an important role in the analyses because brain region should be isolated before other processing algorithms such as tissue classification, registration or cortical surface reconstruction can be made [1–3]. For example, functional images such as Functional magnetic resonance image (FMRI) and Positron Emission Tomography (PET) image usually contain few non-brain tissues, whereas high resolution MR images often contain some non-brain tissues(i.e., skin, fat, muscle, etc.), and if the non-brain tissues of MR images can be accurately removed beforehand, the registration robustness will be improved greatly . Furthermore, 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]. On the other hand, brain extraction is also a difficult and time-consuming pre-processing step performed in neuroimage analysis due to the complexity of human brain anatomy and weak boundaries between brain and non-brain tissues.
Ma et al.  pointed out that researchers should combine the application background with practical requirements to design a proper algorithm for a medical image segmentation task. Although many brain extraction algorithms (BEAs) have been proposed to accurately segment brain from non-brain tissues, their segmentation quality varies greatly and has important influence on the results of subsequent image analysis. Boesen et al.  compared the performance of four BEAs and concluded that the brain extraction tool (BET) and the brain surface extractor (BSE) was significantly faster than the statistical parametric mapping(SPM) or Minneapolis consensus strip(McStrip). Compared to two different manual strip-masks, however, 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]. Subsequent research work are aimed at developing fully automated, accurate and robust BEAs for MR images. To facilitate large-scale neuroimage analysis, Zhuang et al.  developed a new automatic BEA called the model based level set method (MLS) which can provide robust performance for large-scale neuroimage analysis. As the number of subjects increases and the real-time image processing for clinical application develops, the need for fully automated, simple and fast brain extraction algorithms will become critical. In this paper, we first proposed a new method satisfying the requirement of both fully automated brain extraction and accurate brain extraction result; then we summarized the experimental results, evaluation, and comparison of our method to BET and MLS; finally, we discussed the advantages and disadvantages of our method for brain MR image extraction.
We developed an accurate and simple brain extraction method using an improved geometric active contour model (GAC) which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The proposed brain extraction method comprises three major steps: image intensity parameters are first estimated and a binary image of the head is calculated for the following segmentation procedures. Then the initial contour is automatically determined within the brain region. Finally, the proposed geometric active contour model is applied to extract the brain region on each of the slices.
The proposed GAC model with a new local region-based signed pressure force function for brain MR image extraction
The proposed geometric active contour model utilizes the new local region-based SPF function to solve the boundary leaking problem and intensity inhomogeneity which traditional geometric active contour models fail to solve. So our model not only works well for objects that have good contrast but also for objects with weak boundary such as brain surface.
Estimation of image intensity parameters and binary image of the head
Find the most left and right voxels automatically by searching all the voxels with the maximum intensity of the binary image of the head.
Compute the distance d l-r between the most left and right voxels.
Approximate the shape of the brain in axial as a square with its length set to be the distance d l-r , and the shape of the brain in coronal or sagittal orientation as a rectangle with a 8:7 and 4:3 ratio of width (equal to the distance d l-r ) to height, respectively.
Initialize the zero level set curve as a circle and the initial circle is positioned at the center of the square or rectangle with its radius, r, equal to one third of the length of the square side(for axial orientation) or one third of the width of the rectangle(for coronal and sagittal orientation).
- 5.To simplify the initialization of the level set function, we use a binary level set function as in the work of Lie . Each level set function can only take two values at convergence, then the initial function ϕ 0 is defined as:(8)
where ρ is a positive constant, ϕ 0 (x, y) denotes ϕ(x, y, t) at t = 0, Ω is the enclosing interface. With such initialization of the level set function, not only the re-initialization procedure is completely eliminated, but also the level set function ϕ is no longer required to be initialized as a signed distance function.
Correction of leakage through weak boundaries based on local thresholds estimation
Performance comparison of BET, MLS and the proposed method for multiple indices using the IBSR data sets
We proposed an automated and simple brain extraction method using an improved geometric active contour model. Our method has the following advantages over existing brain extraction algorithms: first, Our method uses a binary level set function to eliminate the expensive re-initialization of the existing brain extraction algorithms, it is thus more efficient. Second, the method not only utilizes the image statistical information to construct a new local region-based SPF function, but also corrects the leakage through extremely weak boundaries based on local thresholds estimation, thus can successfully segment brain tissue with weak boundaries. Third, the initial contour can be automatically set inside the brain with sufficiently large radius to improve the automation and the efficiency of the brain extraction. Last but most importantly, our method is very simple and easy to use. No preprocessing step is needed and all the results can be obtained using the original, noisy MR data. Thus, the proposed method can extract brain tissue with high efficiency and full automation compare to two other methods. However, our method was tested using normal adult MRI brain data sets only, and larger sample data sets including different age groups and abnormal anatomy structures such as tumor are needed in order to further test our method as a fully automated, simple and robust method for brain extraction.
This work was supported by the National Natural Science Foundation of China (Grant No. 30670576) and Scientific Research Key Program of Beijing Municipal Commission of Education (Grant No. kz200810025011). The authors would also like to thank the anonymous reviewers for their constructive comments.
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