Title :
Robust fuzzy segmentation of magnetic resonance images
Author_Institution :
Lab. of Personality & Cognition, NIA/NIH, Baltimore, MD, USA
Abstract :
A new approach for the robust segmentation of magnetic resonance images is described. The approach is derived from a generalization of the objective function used in D.L. Pham and J.L. Prince´s (1999) 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 intensity inhomogeneity artifacts as well as noise and other artifacts. The efficacy of the algorithm is demonstrated on simulated magnetic resonance images
Keywords :
adaptive signal processing; fuzzy set theory; image segmentation; magnetic resonance imaging; adaptive fuzzy c-means algorithm; intensity inhomogeneity artifacts; magnetic resonance images; noise; objective function generalization; objective function minimization; robust fuzzy image segmentation; spatially smooth membership functions; unsupervised fuzzy segmentation algorithm; Clustering algorithms; Cognition; Filters; Gerontology; Image segmentation; Laboratories; Magnetic noise; Magnetic resonance; Noise robustness; Smoothing methods;
Conference_Titel :
Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on
Conference_Location :
Bethesda, MD
Print_ISBN :
0-7695-1004-3
DOI :
10.1109/CBMS.2001.941709
