A Statistically Based Surface Evolution Method for Medical Image Segmentation: Presentation and Validation

Abstract. In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.

• Type: Conference
• Year: 2003
• Where: MICCAI
• Authors: Eric Pichon, Allen Tannenbaum, Ron Kikinis
• Keywords: Large Brain Structures, Medical Image Segmentation, Medical Imaging, MICCAI 2003, Real Mri Images