Robust fuzzy segmentation of magnetic resonance images

D. L. Pham

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish (US)
Pages (from-to)127-131
Number of pages5
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
StatePublished - Jan 1 2001
Event14th IEEE Symposium on Computer-Based Medical Systems - Bethesda, MD, United States
Duration: Jul 26 2001Jul 27 2001

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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