A modified FCM algorithm for fast segmentation of brain MR images

L. Szilágyi, S. M. Szilágyi, Z. Benyó

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

Automated brain MR image segmentation is a challenging problem and received significant attention lately. Several improvements have been made to the standard fuzzy c-means (FCM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FCM algorithm, which aims accurate segmentation in case of mixed noises, and performs at a high processing speed. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are classified afterwards using the histogram-based approach of the enhanced FCM classifier. The experiments using synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FCM-based techniques.

Original languageEnglish
Title of host publicationAnalysis and Design of Intelligent Systems using Soft Computing Techniques
PublisherSpringer Verlag
Pages119-127
Number of pages9
ISBN (Print)9783540724315
DOIs
Publication statusPublished - Jan 1 2007

Publication series

NameAdvances in Soft Computing
Volume41
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications

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  • Cite this

    Szilágyi, L., Szilágyi, S. M., & Benyó, Z. (2007). A modified FCM algorithm for fast segmentation of brain MR images. In Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 119-127). (Advances in Soft Computing; Vol. 41). Springer Verlag. https://doi.org/10.1007/978-3-540-72432-2_13