A novel Bayesian approach to adaptive mean shift segmentation of brain images
Paper i proceeding, 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.

Författare

Mahmood Qaiser

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Artur Chodorowski

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Andrew Mehnert

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Mikael Persson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Proceedings - IEEE Symposium on Computer-Based Medical Systems

10637125 (ISSN)

6266304

Ämneskategorier

Medicinteknik

Elektroteknik och elektronik

DOI

10.1109/CBMS.2012.6266304

ISBN

978-146732051-1

Mer information

Skapat

2017-10-07