Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation

Authors:
Peng Yu;Xiao Han;Florent Segonne;Rudolph Pienaar;Randy L. Buckner;Polina Golland;P. Ellen Grant;Bruce Fischl
Affiliations:
MIT, USA;CMS Inc., USA;MGH/MIT/HMS, USA;MGH/MIT/HMS, USA;Harvard University, USA;CSAIL, MIT, USA;Massachusetts General Hospital, USA;MIT, USA
Venue:
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Year:
2006

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Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

Computers in Biology and Medicine

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Abstract

Shape analysis of neuroanatomical structures has proven useful in the study of neuropathology and neurodevelopment. Advances in medical imaging have made it possible to study this shape variation in vivo. In this paper, we propose the use of a spherical wavelet transformation to extract cortical surface shape features, as wavelets can characterize the underlying functions in a local fashion in both space and frequency. Our results demonstrate the utility of the wavelet approach in both detecting the spatial scale and pattern of shape variation in synthetic data, and for quantifying and visualizing shape variations of cortical surface models in subject populations.