Medical Image Processing using Xilinx System Generator

Abstract

Although software versions of different image processing techniques are suitable for general-purpose use,in order to meet the real time applications,an image processing technique needs to be implemented in hardware. FPGAs have many benefits in applications that include digital signal acquisition,but also processing of large data,especially in real time. Mainly due to the ever- decreasing cost and re-configurability,FPGAs have also found its place in digital signal processing (DSP),now replacing ASICs in that area. Xilinx System Generator is a tool from Xilinx that enables the Mathworks Simulink models to be adapted for FPGA design. For comparative study on several levels in edge detection,CT image of a brain with a tumor is used. Performance of gradient based edge detectors - Robert,Prewitt and Sobel was compared. Even from just visual analysis of results,it can be seen that Prewitt and Sobel methods give better results than Robert method. In contrast,the calculation of Robert operator is simpler in comparison to the other operators and occupies less resource,since only adder-subtractor logic is enough to detect the edges. As the implemented algorithms could be part of more complex systems for tumor detection,the design architecture used in this paper can be extended to be used in very complex real time image processing techniques.