Using wavelet transform and neural networks for the analysis of brain MR images

In this study brain MR images are segmented into the constitutive tissues such as the gray matter, white matter and cerebrospinal fluid using multiresolutional wavelet packet transform and self-organizing map networks. For this purpose T1-weighted, T2-weighted and PD-weighted simulated brain MR images are used. First of all, wavelet packet transform is applied to the images. Subimages obtained from the transform are filtered using best subtree method. Feature vector that is used as input to the neural network is constructed by combining the reconstructed images that are the result of the transform. As a consequence brain MR images are segmented into gray matter, white matter and cerebrospinal fluid using self-organizing map networks.

[1]  Bo Hsiao,et al.  Automatic surface inspection using wavelet reconstruction , 2001, Pattern Recognit..

[2]  Javad Alirezaie,et al.  Automatic segmentation of cerebral MR images using artificial neural networks , 1996 .

[3]  Esa Alhoniemi,et al.  Self-organizing map in Matlab: the SOM Toolbox , 1999 .

[4]  Aly A. Farag,et al.  Two-stage neural network for volume segmentation of medical images , 1997, Pattern Recognit. Lett..

[5]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[6]  Richard A. Robb,et al.  Biomedical Imaging, Visualization, and Analysis , 1999 .

[7]  Edwin N. Cook,et al.  Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks , 1997, IEEE Transactions on Medical Imaging.

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  B. Michaelis,et al.  DETECTION OF TUMOR IN DIGITAL IMAGES OF THE BRAIN , 2001 .

[10]  Ronald R. Coifman,et al.  Wavelet analysis and signal processing , 1990 .

[11]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[12]  Lalit M. Patnaik,et al.  Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..

[13]  D. Louis Collins,et al.  Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases , 2006, IEEE Transactions on Medical Imaging.