Fuzzy classification of brain MRI using a priori knowledge: Weighted fuzzy C-means

Salvado, Olivier, Bourgeat, Pierrick, Tarnayo, Oscar Acosta, Zuluaga, Maria and Ourselin, Sebastien (2007). Fuzzy classification of brain MRI using a priori knowledge: Weighted fuzzy C-means. In: 2007 IEEE 11th International Conference On Computer Vision, Vols 1-6. 11th IEEE International Conference on Computer Vision (OMNIVIS 2007), Rio de Janeiro, Brazil, (2548-2555). 14-21 October 2007. doi:10.1109/ICCV.2007.4409155


Author Salvado, Olivier
Bourgeat, Pierrick
Tarnayo, Oscar Acosta
Zuluaga, Maria
Ourselin, Sebastien
Title of paper Fuzzy classification of brain MRI using a priori knowledge: Weighted fuzzy C-means
Conference name 11th IEEE International Conference on Computer Vision (OMNIVIS 2007)
Conference location Rio de Janeiro, Brazil
Conference dates 14-21 October 2007
Proceedings title 2007 IEEE 11th International Conference On Computer Vision, Vols 1-6   Check publisher's open access policy
Journal name 2007 Ieee 11th International Conference On Computer Vision, Vols 1-6   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2007
Sub-type Fully published paper
DOI 10.1109/ICCV.2007.4409155
ISBN 978-1-4244-1630-1
ISSN 1550-5499
Start page 2548
End page 2555
Total pages 8
Language eng
Abstract/Summary We report in this communication a new formulation for the cost function of the well-known fuzzy C-means classification technique whereby we introduce weights. We derive the equations of this new weighted fuzzy C-means algorithm (WFCM) in the presence of additive and multiplicative bias field. We show that the weights can be designed in the same manner as prior probabilities commonly used in maximum a posteriori classifier (MAP) to introduce prior knowledge (e.g. using atlas), and increase robustness to noise (e.g. using Markov random field). Using prior probabilities of three popular MAP algorithms, we compare the performances of our proposed WFCM scheme using the simulated MRI T1W BrainWeb datasets, as well as five T1W MR patient scans. Our results show that WFCM achieves superior performances for low SNR conditions, whereas a Gaussian mixture model is desirable for high noise levels. WFCM allows rigorous comparison of fuzzy and probabilistic classifiers, and offers a framework where improvements can be shared between those two types of classifier.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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