Abstract
It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.
Highlights
Background removalAs the first step to acquire a rough brain mask, the proposed method separated the brain regions from the background in the axial slices using the image h istogram[27]
To validate the proposed algorithm, we used T2 infant brain images, which were previously acquired with a 2D Turbo Spin Echo (TSE) sequence and a 3D TSE BLADE sequence
We proposed an algorithm that can be used to acquire an accurate brain mask from the T2-weighted magnetic resonance (MR) images
Summary
As the first step to acquire a rough brain mask, the proposed method separated the brain regions from the background in the axial slices using the image h istogram[27]. In an MR image histogram, the object image can be roughly distinguished from the background because they are represented as high peaks in the clusters of the intensities. Based on the histogram partitioning, concavities around the main peaks of a histogram can be detected, which can be used to separate the brain region from non-brain regions as follows. To partition the histogram H(x) of an MR image, we use a Gaussian graph P(x), defined on the same gray level as H(x) (Fig. 1). P(x) is defined as a normal distribution, having the identical mean (μ) and standard deviation (α) as H(x), so that the areas under the graphs are identical for P(x) and H(x).
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