An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images

Authors:
Jing-Hao Xue;Aleksandra Pizurica;Wilfried Philips;Etienne Kerre;Rik Van De Walle;Ignace Lemahieu
Affiliations:
Department of Telecommunications and Information Processing, Ghent University, B9000 Gent, Belgium;Department of Telecommunications and Information Processing, Ghent University, B9000 Gent, Belgium;Department of Telecommunications and Information Processing, Ghent University, B9000 Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, B9000 Gent, Belgium;Department of Electronics and Information Systems, Ghent University, B9000 Gent, Belgium;Department of Electronics and Information Systems, Ghent University, B9000 Gent, Belgium
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

This paper presents an integrated method of the adaptive enhancement for an unsupervised global-to-local segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. Three brain tissues are of interest: CSF (CerebroSpinal Fluid), GM (Gray Matter), WM (White Matter). Firstly, we de-noise the images using a newly proposed versatile wavelet-based filter, and segment the images with minimum error global thresholding. Subsequently, we combine a spatial-feature-based FCM (Fuzzy C-Means) clustering with 3-D clustering-result-weighted median and average filters, so as to further achieve a locally adaptive enhancement and segmentation. This integrated strategy yields a robust and accurate segmentation, particularly in noisy images. The performance of the proposed method is validated by four indices on MRI brain phantom images and on real MRI images.