Title :
A novel method for adaptive enhancement and unsupervised segmentation of MRI brain image
Author :
Xue, Jing-Hao ; Philips, Wilfried ; Pizurica, Aleksandra ; Lemahieu, Ignace
Author_Institution :
Dept. of Telecommun. & Inf. Process., Ghent Univ., Gent, Belgium
Abstract :
This paper describes a novel global-to-local method for the adaptive enhancement and unsupervised segmentation of brain tissues in MRI (magnetic resonance imaging) images. Three brain tissues are of interest: CSF (cerebrospinal fluid), GM (gray matter), WM (white matter). Firstly, we de-noise the image using wavelet thresholding, and segment the image with minimum error thresholding. Both the thresholdings are global-wise. Subsequently, we combine locally adaptive weighted median and weighted average filters with FCM (fuzzy C-means) clustering to achieve a local-wise segmentation. The performance of the proposed method is quantitatively validated by four indices with respect to a MRI brain phantom
Keywords :
adaptive filters; adaptive signal processing; biomedical MRI; brain; filtering theory; fuzzy systems; image enhancement; image segmentation; median filters; medical image processing; pattern clustering; wavelet transforms; FCM clustering; MRI brain image; MRI brain phantom; adaptive image enhancement; brain tissues; cerebrospinal fluid; fuzzy C-means; gray matter; image de-noising; locally adaptive weighted average filter; locally adaptive weighted median filter; magnetic resonance imaging; minimum error thresholding; unsupervised image segmentation; wavelet thresholding; white matter; Adaptive filters; Brain; Image segmentation; Information processing; Information systems; Magnetic resonance imaging; Noise reduction; Pixel; Signal to noise ratio; Wavelet domain;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.941344
