Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation

László Szilágyi, Sándor M. Szilágyi, Balázs Benyó, Z. Benyó

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a hybrid c-means clustering approach to replace the FCM algorithm found in several existing solutions. The novel clustering model is assisted by a pre-filtering technique for Gaussian and impulse noise elimination, and a smoothening filter that helps the c-means algorithm at the estimation of inhomogeneity as a slowly varying additive or multiplicative noise. The slow variance of the estimated INU is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show that the proposed method provides more accurate and more efficient segmentation than the FCM based approach. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Pages204-209
Number of pages6
Volume7
EditionPART 1
DOIs
Publication statusPublished - 2009
Event7th IFAC Symposium on Modelling and Control in Biomedical Systems (including Biological Systems) MCBMS'09 - Aalborg, Denmark
Duration: Aug 12 2009Aug 14 2009

Other

Other7th IFAC Symposium on Modelling and Control in Biomedical Systems (including Biological Systems) MCBMS'09
CountryDenmark
CityAalborg
Period8/12/098/14/09

Fingerprint

Image segmentation
Brain
Gaussian noise (electronic)
Impulse noise
Image registration
Clustering algorithms
Experiments
Compensation and Redress

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Szilágyi, L., Szilágyi, S. M., Benyó, B., & Benyó, Z. (2009). Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. In IFAC Proceedings Volumes (IFAC-PapersOnline) (PART 1 ed., Vol. 7, pp. 204-209) https://doi.org/10.3182/20090812-3-DK-2006.0089

Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. / Szilágyi, László; Szilágyi, Sándor M.; Benyó, Balázs; Benyó, Z.

IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 7 PART 1. ed. 2009. p. 204-209.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Szilágyi, L, Szilágyi, SM, Benyó, B & Benyó, Z 2009, Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. in IFAC Proceedings Volumes (IFAC-PapersOnline). PART 1 edn, vol. 7, pp. 204-209, 7th IFAC Symposium on Modelling and Control in Biomedical Systems (including Biological Systems) MCBMS'09, Aalborg, Denmark, 8/12/09. https://doi.org/10.3182/20090812-3-DK-2006.0089
Szilágyi L, Szilágyi SM, Benyó B, Benyó Z. Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. In IFAC Proceedings Volumes (IFAC-PapersOnline). PART 1 ed. Vol. 7. 2009. p. 204-209 https://doi.org/10.3182/20090812-3-DK-2006.0089
Szilágyi, László ; Szilágyi, Sándor M. ; Benyó, Balázs ; Benyó, Z. / Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 7 PART 1. ed. 2009. pp. 204-209
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