NURBS-Based Segmentation of the Brain in Medical Images
R.G.N. Meegama, Jagath Chandana Rajapakse · International Journal of Pattern Recognition and Artificial Intelligence · 2003
Extracting the human brain from magnetic resonance head scans is difficult because of its highly convoluted and nonuniform geometry. A technique based on Non-Uniform Rational B-Splines (NURBS) surfaces and energy minimizing deformable models to extract and visualize the brain surface patterns accurately from magnetic resonance head scans is presented. The weighting parameter that comes with the NURBS definition is explored to attract the surface into regions showing high curvature. The weight at each control point is adjusted automatically according to the curvature properties of the evolving surface. This process facilitates a deformable model with increased local flexibility that adapts to complex geometrical features of the brain surface. The results show that the proposed model is capable of capturing the correct brain surface with a higher accuracy than the existing techniques.