MR Brain Image Segmentation Using an Enhanced Fuzzy C-Means Algorithm

L. Szilágyi, Z. Benyó, S. M. Szilágyi, H. S. Adam

Research output: Contribution to journalConference article

307 Citations (Scopus)

Abstract

This paper presents a new algorithm for fuzzy segmentation of MR brain images. Starting from the standard FCM [1] and its bias-corrected version BCFCM [2] algorithm, by splitting up the two major steps of the latter, and by introducing a new factor γ, the amount of required calculations is considerably reduced. The algorithm provides good-quality segmented brain images a very quick way, which makes it an excellent tool to support virtual brain endoscopy. This research has been supported by the Hungarian National Research Fund, Grants No. OTKA T042990 and T029830.

Original languageEnglish
Pages (from-to)724-726
Number of pages3
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
Publication statusPublished - Jan 1 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

Keywords

  • Fuzzy Logic
  • Image Segmentation
  • MR Imaging

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'MR Brain Image Segmentation Using an Enhanced Fuzzy C-Means Algorithm'. Together they form a unique fingerprint.

  • Cite this