Analyzing Wavelet and Bidimensional Empirical Mode Decomposition of MRI Segmentation using Fuzzy C-Means Clustering

Gulam Sarwar Chuwdhury, Md. Khaliluzzaman, Md. Rashed-Al Mahfuz
2016 Rajshahi University Journal of Science and Engineering  
Image segmentation is a vital step in medical image processing. Magnetic resonance<br />imaging (MRI) is used for brain tissues extraction in white and gray matter. These tissues<br />extraction help in image segmentation applications such as radiotherapy planning, clinical<br />diagnosis, treatment planning. This paper presents utilization of fuzzy C-means (FCM)<br />clustering by using wavelet and bidimensional empirical mode decomposition (BEMD) to<br />improve the quality of noisy MR
more » ... The signal to noise ratio (SNR) value is<br />calculated from FCM clustering data to examine the best segmentation technique. The<br />experiment with synthetic Brain Web images has demonstrated the efficiency and<br />robustness of the appropriate approach in segmenting medical MRI.
doi:10.3329/rujse.v44i0.30395 fatcat:chtxg6rk4fbynmhfqnowbfveyy