Robust fuzzy c-means clustering algorithm using non-parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation

• Published in: IET image processing Vol. 12; no. 5; pp. 652 - 660
• Main Authors: Chetih, Nabil, Messali, Zoubeida, Serir, Amina, Ramou, Naim
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2018
• Subjects: Bayes methods; biomedical MRI; brain; estimation theory; extended FCM algorithm; fuzzy set theory; good quality image recovery; image segmentation; medical image processing; noisy MR brain image segmentation; noisy wavelet coefficients; nonparametric Bayesian estimation; nonparametric statistics; pattern clustering; priori statistical model; real magnetic resonance imaging brain image; Research Article; robust fuzzy C‐means clustering algorithm; statistical analysis; wavelet transform domain; wavelet transforms; wavelet‐based Bayesian statistical estimation; biomedical MRI; real magnetic resonance imaging brain image; estimation theory; wavelet transforms; nonparametric Bayesian estimation; wavelet transform domain; fuzzy set theory; robust fuzzy C-means clustering algorithm; brain; noisy MR brain image segmentation; priori statistical model; noisy wavelet coefficients; wavelet-based Bayesian statistical estimation; pattern clustering; image segmentation; good quality image recovery; Bayes methods; extended FCM algorithm; nonparametric statistics; statistical analysis; medical image processing
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2017.0399

The major drawback of the fuzzy c-means (FCM) algorithm is its sensitivity to noise. The authors propose a new extended FCM algorithm based a non-parametric Bayesian estimation in the wavelet transform domain for segmenting noisy MR brain images. They use the Bayesian estimator to process the noisy wavelet coefficients. Before segmentation based on FCM algorithm, they use an a priori statistical model adapted to the modelisation of the wavelet coefficients of a noisy image. The main objective of this wavelet-based Bayesian statistical estimation is to recover a good quality image, from a noisy image of poor quality. Experimental results on simulated and real magnetic resonance imaging brain images show that their proposed method solves the problem of sensitivity to noise and offers a very good performance that outperforms some FCM-based algorithms.