Authors : T. Justin Jose and P. Mythili
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.
T. Justin Jose and P. Mythili , 2008. fMRI Segmentation Using Echo State Neural Network . International Journal of Soft Computing, 3: 38-43.