Research On The Optimal Algorithm, Model And Its Application In Brain Functional Imaging

The high function of human brain is one of the most complex activities in the world of nature. In the research of high brain function, we need gain the reflection of every brain area when they are stimulated. With the instruments such as PET, SPECT and fMRI etc, we can obtain the images with high resolution that we need in the experiment of high brain functional imaging. It is inevitable that there is some interference noise, positional shift and distortion of image caused by the movement of tested person, so effective signal detection and image processing technology is indispensable.Brain functional imaging is a kind of important tools in study of brain and life science. In research, we often need analysis the result of multimodal medical images of a single patient or time series images in combination, so it is necessary to register the brain functional images. Based on Gauss-Newton method, this paper proposes a practical algorithm when we search for the direction p in solving nonlinear least square problem. By using this algorithm, we needn't resolve the inverse matrix in Gauss-Newton method, so we can get rid of the rounding error, by means of points-based register method, realize the higher precision registration of brain functional images.In addition, it is easy to reach a local extreme value when using Gauss-Newton method, so a proper initial iterative value is requested. On the other hand, Simulated Annealing Algorithm almost has no special need for the selection of initial iterative value, so the corresponding discussion about this algorithm is mentioned in the following of this paper.Last of all, comparison and analysis between Gauss-Newton method and simulated annealing algorithm are provided based on real computation instance.