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A novel Bayesian approach to adaptive mean shift segmentation of brain images

Mahmood Qaiser (Institutionen för signaler och system, Digitala bildsystem och bildanalys) ; Artur Chodorowski (Institutionen för signaler och system, Digitala bildsystem och bildanalys) ; Andrew Mehnert (Institutionen för signaler och system, Digitala bildsystem och bildanalys) ; Mikael Persson (Institutionen för signaler och system, Medicinska signaler och system)
Proceedings - IEEE Symposium on Computer-Based Medical Systems. 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012, Rome, 20 - 22 June 2012 (1063-7125). (2012)
[Konferensbidrag, refereegranskat]

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.



Denna post skapades 2012-10-30. Senast ändrad 2013-06-28.
CPL Pubid: 165239

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Digitala bildsystem och bildanalys (1900-2013)
Institutionen för signaler och system, Medicinska signaler och system

Ämnesområden

Elektroteknik och elektronik
Medicinteknik

Chalmers infrastruktur

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