@article{MAKHILLIJSC20083120888,
    title = {fMRI Segmentation Using Echo State Neural Network},
    journal = {International Journal of Soft Computing},
    volume = {3},
    number = {1},
    pages = {38-43},
    year = {2008},
    issn = {1816-9503},
    doi = {ijscomp.2008.38.43},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2008.38.43},
    author = {T. Justin Jose and},
    keywords = {Echo State Neural Network (ESNN),Intelligent segmentation,functional Magnetic Resonance Imaging (fMRI),Back-Propagation Algorithm (BPA),Feature Extraction,Peak Signal to Noise Ratio (PSNR)},
    abstract = {This research work proposes a new intelligent segmentation technique for functional Magnetic Resonance  Imaging (fMRI).  It has been implemented using an Echostate Neural Network (ESNN). Segmentation is an important imaging process that helps in identifying objects of the image. Existing segmentation methods are not able to exactly segment the complicated profile of the fMRI accurately. Segmentation of every pixel in the  fMRI  correctly helps in proper location of  tumor.  The presence of noise and  artifacts  poses a challenging problem in proper segmentation. The proposed ESNN is an estimation method  with  energy  minimization. The estimation property helps in better segmentation of the complicated profile of the fMRI. The  performance of  the new segmentation method is found to be better with higher Peak Signal to Noise Ratio (PSNR) of 61 when compared to the PSNR of the existing Back-Propagation Algorithm (BPA) segmentation method, which  is  57.}
    }