From experiments, it is shown that the Co-occurrence matrix for one still MRI brain image does not provide enough information for segmentation. The 6D Co-occurrence image segmentation idea for 3D MRI image is modified and implemented in 2D MRI image segmentation. That idea is to take two or three images as input at the same time and then process them with 3D Co-occurrence matrix. With this kind of processing a lot of information was brought into the Co-occurrence matrix, which is enough to segment the images. To compare the result, some other segmentation ideas were tested in this project. From the results, it can be seen that the MRI image segmentation based on the Co-ocurrence texture analysis with two images or three images sampling is practical and the result satisfying. The segmentation is simulated in MATLAB. After the simulation, the segmentation is implemented in FPGA using VHDL. MODELSIM is used for FPGA functionality simulation. The result is close the MATLAB simulation. This makes it possible to implement the system with FPGA hardware.
Synthesis of 3D MRI Brain Images With Shape and Texture Generative Adversarial Deep Neural Networks