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@article{146167,
        author = {Prajakta Bhimrao Rathod  and Shailesh V. Bhalerao},
        title = {Review On Implementation of CGRA based 3D fMRI filter for Alzheimer disease },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {1754-1758},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146167},
        abstract = {The function magnetic resonance imaging (FMRI) is fast growing imaging technology in the study of neuro-disease such as Alzheimer .For the disease classification, several de-noising pre-processing filter techniques is used to nullify effect of noise and artifacts in FMRI disease image. In this regards, many FPGA based hardware processing architecture is used for designing classification system such as Fine grain reconfigurable architecture, Application Specific Integrated Circuit (ASIC) and CGRA. In Fine grain reconfigurable architecture, FPGAs hardware modeling are slower and less power efficient due finer granularity’s as it operate at bit-level configuration. To overcome this problem, the concept of Coarse Grained Reconfigurable Architectures (CGRAs) is introduced whose granularity is at word-level (processing unit). In this hardware modeling, pre-processing filter (median, Non local maximum likelihood ) is designed with CGRA configuration and compare processed result with different parametric analysis. it is observed that the overall performance is optimized in terms of (Mini-mental State Examination (MMSE), Root Mean Square Error (RMSE), Peak Signal-to-noise ratio (PSNR), ,over Fine grain reconfigurable architecture. },
        keywords = {Function magnetic resonance imaging [FMRI], Coarse Grained Reconfigurable Architectures [CGRAs], Application Specific Integrated Circuit (ASIC),Non-local maximum likelihood filter (NLML).},
        month = {},
        }