Title :
Wavelet Shrinkage Prefiltering for Brain Tissue Segmentation
Author :
Hou, Zujun ; Koh, Tong San
Author_Institution :
Biomed. Imaging Lab, Biomed. Sci. Inst.
Abstract :
This paper presents a method to segment brain tissue from T1-weighted magnetic resonance (MR) images. A modified BayesShrink method is utilized to filter the image in wavelet transform domain before segmentation, where the shrinkage strength is automatically adjusted with respect to noise level. Then the fuzzy c-means clustering is applied to segment brain tissue into cerebrospinal fluid, gray matter and white matter. Comparison with other methods for brain tissue segmentation that remove noise using wavelet or non-wavelet based methods is made on phantom or real data and the advantage of the proposed method is demonstrated
Keywords :
biomedical MRI; brain; fuzzy set theory; image denoising; image segmentation; medical image processing; phantoms; statistical analysis; wavelet transforms; BayesShrink method; T1-weighted magnetic resonance images; brain tissue segmentation; cerebrospinal fluid; fuzzy c-means clustering; gray matter; noise removal; phantom; wavelet shrinkage prefiltering; wavelet transform; white matter; Brain; Computational efficiency; Filters; Image segmentation; Imaging phantoms; Magnetic resonance; Noise level; Noise reduction; Wavelet domain; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616744
