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Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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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Authors and Affiliations

  1. Biomedical Imaging Lab, Singapore Bioimaging Consortium, A*Star, 30 Biopolis Street, Matrix#07-01, Singapore, 138671, Singapore

    Zujun Hou & Su Huang

  2. Institute for Infocomm Research, A*Star, 21 Heng Mui Keng Terrace, Singapore, 119613, Singapore

    Zujun Hou

Authors
  1. Zujun Hou
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  2. Su Huang
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Correspondence to Su Huang.

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Hou, Z., Huang, S. Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images. Int J CARS 2, 305–316 (2008). https://doi.org/10.1007/s11548-007-0144-y

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  • DOI: https://doi.org/10.1007/s11548-007-0144-y

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