Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps
Journal article, 2015

This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means. Mean shift is employed to cluster the tissues in the joint spatial-intensity feature space and then a fuzzy c-means is applied with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on a synthetic T1-weighted MR image with varying noise characteristics and spatial intensity inhomogeneity, obtained from the BrainWeb database as well as on 38 real T1-weighted MR images, obtained from the IBSR repository. The performance of the proposed framework is evaluated relative to the three widely used brain segmentation toolboxes: FAST, SPM and PVC, and the adaptive mean shift (AMS) and classical fuzzy c-means methods. The experimental results demonstrate the robustness of the proposed framework, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real T1-weighted MR images compared to all competing methods.

magnetic resonance imaging

mean shift

Segmentation

Author

Mahmood Qaiser

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Artur Chodorowski

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Mikael Persson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

IRBM

1959-0318 (ISSN)

Vol. 36 3 185-196

Subject Categories

Medical Image Processing

DOI

10.1016/j.irbm.2015.01.007

More information

Created

10/7/2017