Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces

Authors:
Li Wang;Chunming Li;Quansen Sun;Deshen Xia;Chiu-Yen Kao
Affiliations:
School of Computer Science & Technology, Nanjing University of Science and Technology, China;Institute of Imaging Science, Vanderbilt University, USA;School of Computer Science & Technology, Nanjing University of Science and Technology, China;School of Computer Science & Technology, Nanjing University of Science and Technology, China;Department of Mathematics, The Ohio State University, USA
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

In this paper, we present an improved region-based active contour/surface model for 2D/3D brain MR image segmentation. Our model combines the advantages of both local and global intensity information, which enable the model to cope with intensity inhomogeneity. We define an energy functional with a local intensity fitting term and an auxiliary global intensity fitting term. In the associated curve evolution, the motion of the contour is driven by a local intensity fitting force and a global intensity fitting force, induced by the local and global terms in the proposed energy functional, respectively. The influence of these two forces on the curve evolution is complementary. When the contour is close to object boundaries, the local intensity fitting force became dominant, which attracts the contour toward object boundaries and finally stops the contour there. The global intensity fitting force is dominant when the contour is far away from object boundaries, and it allows more flexible initialization of contours by using global image information. The proposed model has been applied to both 2D and 3D brain MR image segmentation with promising results.