A novel Bayesian approach to adaptive mean shift segmentation of brain images
Paper in proceedings, 2012

We present a novel adaptive mean shift (AMS) algorithm for the segmentation of tissues in magnetic resonance (MR) brain images. In particular we introduce a novel Bayesian approach for the estimation of the adaptive kernel bandwidth and investigate its impact on segmentation accuracy. We studied the three class problem where the brain tissues are segmented into white matter, gray matter and cerebrospinal fluid. The segmentation experiments were performed on both multi-modal simulated and real patient T1-weighted MR volumes with different noise characteristics and spatial inhomogeneities. The performance of the algorithm was evaluated relative to several competing methods using real and synthetic data. Our results demonstrate the efficacy of the proposed algorithm and that it can outperform competing methods, especially when the noise and spatial intensity inhomogeneities are high.

Author

Mahmood Qaiser

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

Artur Chodorowski

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

Andrew Mehnert

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

Proceedings - IEEE Symposium on Computer-Based Medical Systems

10637125 (ISSN)

6266304

Subject Categories

Medical Engineering

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/CBMS.2012.6266304

ISBN

978-146732051-1

More information

Created

10/7/2017