MRI brain image segmentation by multi-resolution edge detection and region selection.

Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multi-resolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rae-Hong Park,et al.  Multiresolution edge detection techniques , 1995, Pattern Recognit..

[3]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[4]  Narendra Ahuja,et al.  Multiscale image segmentation by integrated edge and region detection , 1997, IEEE Trans. Image Process..

[5]  Rachid Deriche,et al.  3D edge detection using recursive filtering: Application to scanner images , 1991, CVGIP Image Underst..

[6]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[7]  Jiebo Luo,et al.  Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications , 1998, IEEE Trans. Image Process..

[8]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[9]  Zhi-Pei Liang,et al.  Template deformation by maximizing mutual information , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[10]  E T Bullmore,et al.  A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images. , 1999, Magnetic resonance imaging.