Effect of non-local means denoising on cortical segmentation accuracy with FACE

Simon F. Eskildsen (1, 2) , Pierrick Coupé (1) , Vladimir Fonov (1) , Lasse Riis Ostergaard (2) , Louis D. Collins (1)

1 MNI - McConnell Brain Imaging Centre
2 AAU - Aalborg University [Denmark]

Cortical thickness measurements based on MRI have the potential to accurately detect changes in the cortical gray matter. During the last decade, several methods have been developed to estimate cortical thickness using surface based reconstruction. Due to the high sensitivity of reconstruction methods to acquisition artifacts, noise in MRI signal may directly affect the obtained measurements. Usually, this noise is reduced using various smoothing filters. Recently, the non-local means (NLM) filter has demonstrated very high denoising performance compared to these traditional filters. In this study, we investigated the sensitivity to noise of cortical surface reconstruction and the ability of the NLM filter to reduce the effect of noise.

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Simon F. Eskildsen, Pierrick Coupé, Vladimir Fonov, Lasse Riis Ostergaard, Louis D. Collins. Effect of non-local means denoising on cortical segmentation accuracy with FACE. Organization for Human Brain Mapping 2011 Annual Meeting, Jun 2011, Canada. ⟨hal-00645491⟩