Level Set Brain

Leonid Zhukov, David Breen, Al Barr

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active contours/deformable models on a gray-scale MRI data. Recently, we proposed a technique that uses diffusion properties of tissue available from DT-MRI to segment out brain structures. We develop a computational pipeline starting from raw diffusion tensor data, through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3D segmentation of DT-MRI datasets. We use a level set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they may be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions.

L. Zhukov, K. Museth, D. Breen, A.H. Barr and R. Whitaker,
Level Set Segmentation and Modeling of DT-MRI Human Brain Data.
Journal of Electronic Imaging, vol 12, pp 125-133, 2003.

Raw Tensor Data

Diffusivity Anisotropy

Combined model of ventricles and (semi-transparent) anisotropic regions: rear, exploded view (left), bottom view (right), side view (bottom). Note how model of ventricles extracted from isotropic measure dataset V1 fits into model extracted from anisotropic measure dataset V2

Segmentation using isotropic V1 (yop row) and anisotropic V2 (bottom row) measure datasets. Images on the left are results of marching cubes iso-surfacing, images on the right obtained by level set segmentation of the same datasets