Open AccessRobust fuzzy c‐means clustering algorithm using non‐parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation
Author(s) -
Chetih Nabil,
Messali Zoubeida,
Serir Amina,
Ramou Naim
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0399
Subject(s) - pattern recognition (psychology) , artificial intelligence , wavelet , wavelet transform , computer science , image segmentation , bayesian probability , cluster analysis , fuzzy logic , noise (video) , segmentation , mathematics , algorithm , computer vision , image (mathematics)
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.