Abstract
A new approach for robust segmentation of magnetic resonance images is described. The approach is derived from a generalization of the objective function used in Pham and Prince's Adaptive Fuzzy C-means algorithm (AFCM). Within the objective function, an additional constraint is placed on the membership functions that forces them to be spatially smooth. Minimization of this objective function results in an unsupervised fuzzy segmentation algorithm that is robust to both intensity inhomogeniety artifacts as well as noise and other artifacts. The efficacy of the algorithm is demonstrated on simulated magnetic resonance images.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 127-131 |
| Number of pages | 5 |
| Journal | Proceedings of the IEEE Symposium on Computer-Based Medical Systems |
| State | Published - Jan 1 2001 |
| Event | 14th IEEE Symposium on Computer-Based Medical Systems - Bethesda, MD, United States Duration: Jul 26 2001 → Jul 27 2001 |
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications