A statistically based flow for image segmentation.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 15450221.
- Also identified by DOI 10.1016/j.media.2004.06.006 and PMC identifier 3652279.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is versatile, 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.
Medical subject headings
- Algorithms
- Brain Mapping
- Brain Neoplasms
- Image Interpretation, Computer-Assisted
- Imaging, Three-Dimensional
- Models, Statistical