FPGA hardware architecture of correlation-based MRI images classification using XSG

Medical imaging classification is one of the areas where using algorithm-based hardware architecture improves performance, in terms of time processing. It gives better and clearer results than when using software implementation. Today, advantages of field-rogrammable gate array (FPGA), including reusability, filed reprogramability, simpler design cycle, fast marketing and a combination of the main advantages of ASICs and DSPs make them powerful and very attractive devices for rapid prototyping of all images processing applications. In this paper, we use Xilinx system generator (XSG) environment to develop a hardware classification-based correlation algorithm from a system level approach. This architecture may be of great influence on the final choice to prove if the MRI image is with lesions brain or normal. Results are illustrated on a simple example for brain magnetic resonance imaging (MRI) images classification. Two sets are used: a set of normal MR images and another set with MR lesion brain images.