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

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.

Inspec keywords: image segmentation; medical image processing; pattern clustering; statistical analysis; Bayes methods; wavelet transforms; brain; biomedical MRI; nonparametric statistics; estimation theory; fuzzy set theory

Other keywords: priori statistical model; nonparametric Bayesian estimation; extended FCM algorithm; noisy wavelet coefficients; robust fuzzy C-means clustering algorithm; good quality image recovery; wavelet-based Bayesian statistical estimation; noisy MR brain image segmentation; real magnetic resonance imaging brain image; wavelet transform domain

Subjects: Medical magnetic resonance imaging and spectroscopy; Other topics in statistics; Optical, image and video signal processing; Computer vision and image processing techniques; Other topics in statistics; Integral transforms; Patient diagnostic methods and instrumentation; Combinatorial mathematics; Integral transforms; Probability theory, stochastic processes, and statistics; Biomedical magnetic resonance imaging and spectroscopy; Biophysics of neurophysiological processes; Algebra, set theory, and graph theory; Biology and medical computing; Combinatorial mathematics

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0399
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