VLSI implementation of modified daubechies architecture for brain disease detection and identification

In the human brain pathology the quantitative analysis of magnetic resonance imaging (MRI) data is important in both research and clinical point of veiw. Generally the MRI images are very noisy. Hence it is quite difficult for the doctors to diagnose and conclude on any disease. In the recent research, various images de-noising algorithms have been proposed. As far as the brain diseases are concerned, noise reduction alone doesn´t suffice. Doctors who view the MRI images finally conclude the disease with the knowledge that they have. This paper aims at developing an efficient VLSI system, which can help the doctors to diagnose and conclude on a particular brain disease. The proposed system implements the modified Daubechies algorithm for feature extraction and brain disease identification based on the MRI images. The VLSI system is implemented on the FPGA as it provides flexibility of reconfigurability and reprogrammability and it is a real time solution.