Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images |
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Authors: | Zujun Hou Su Huang |
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Institution: | (1) Biomedical Imaging Lab, Singapore Bioimaging Consortium, A*Star, 30 Biopolis Street, Matrix#07-01, Singapore, 138671, Singapore;(2) Institute for Infocomm Research, A*Star, 21 Heng Mui Keng Terrace, Singapore, 119613, Singapore |
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Abstract: | Objective
Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology
and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized
this process and suggest several improvements.
Materials and methods
To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes
denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising
phase employs a 3D-extension of the Bayes–Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et
al.’s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain
tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive
comparisons with other sequential or iterative methods have been carried out using simulated and real images.
Results
The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed,
but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels.
Conclusion
A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal,
inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF,
GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline
methods. |
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Keywords: | Brain tissue segmentation Denoising Inhomogeneity correction Thresholding |
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