Research On The Medical Image Fusion Based On GPU Hardware Acceleration

Medical image fusion is a multi-disciplinary subject and an important application of digital image processing in biomedical engineering that relates to image and signal processing as well as medical imaging and clinical diagnosis. This subject combines distinguished characters of different data sources into one image, and represents their relationship of space to help doctors to diagnose patents'illnesses and arrange their post hospitalization treatments more precisely and comprehensively. Fusion and visualization of medical images plays an important role in diagnosis, surgery planning and simulating, radiotherapy planning and teaching in anatomy. Thus, study and research on the medical image fusion have significance on science and worthiness in practical application. However, since medical image fusion technical has not done well in both accuracy and speed to restrict its field of application.With the advance in computer hardware, the computation power of graphics processing unit (GPU) is growing exponentially in the latest several years. Today its applied range is not only limited in graphics rendering, but also extends to some general purpose computing by its excellent float-point arithmetic capacity, flexible programming and parallel architecture. This is led to the so-called GPGPU technology.The main subject of this thesis is to propose and demonstrate a new multiple dimensioned arithmetic named frequency domain Nonsubsampled Contourlet transform. And apply this arithmetic to medical image fusion and accelerate the whole process by GPU. This algorithm builds a simple and high-speed Nonsubsampled Contourlet transform in frequency domain to eliminate the ringing and Pseudo-Gibbs phenomena caused by Wavelet or Contourlet Transformation. The thesis has also done some research on the application technology of medical image visualization, fusion, graphics and general purpose computing. Finally, the thesis introduces the specific image fusion process and compare the performances between different platforms and algorithms, which indicates that this fusion method can provide great definition and acceleration for medical image fusion.