Image Analysis Technique Based On PSO Algorithm

The analysis of Wrist force has always been a key points and difficulties problemsin the field of medicine. This article uses pressure sensitive piece and image processingto solve this difficult problems. The particle swarm optimization can intuitively reflectthe force size of human wrist，so that the trauma of human wrist can get well cure. Todeal with the image of the human wrist, getting human wrist force analysis, this articlepropose an image analysis method based on Particle Swarm Optimization.PSO algorithm is the short of Particle Swarm Optimization. The meaning oftranslation into Chinese is the Particle Swarm Optimization algorithm. Particle SwarmOptimization (PSO) algorithm is originated from the study of simple life groups.The expansion of the PSO algorithm has good application in a lot of ways. Andthe image segmentation is one of them. Its main characteristic is don’t need too manyparameters, also don’t need too much knowledge, and do not need to be very complexprinciple. Meanwhile, the convergence speed is very fast, which solved the problemscaused by traditional optimization algorithm. Particle swarm optimization (PSO) nowhas a widespread application in each domain, and we all known as the focus ofresearch the scientific workers.Every individual in the PSO algorithm was seen as a no quality of the particle.And the fitness of each particle has its own value. Besides, it also contains a speed,which including the direction of motion and the speed of movement. Each particle inthe problem space searching for the two extreme value, namely the individual bestposition and best position.Particles have a property called adaptive value, which is determined by therequired optimization function. in addition to, all of the particles have a specificattributes of the V, which is used to determine the direction and length. Through thisproperty, the particle will traverse in the direction of the optimal solution until we findthe optimal solution.At the beginning of particle swarm optimization (PSO), the particles areinitialized a point.Then after a time the iterative updating parameters, and the optimalsolution is infinite.It ultimately come to the conditions and convergence. After every cycle of time, the algorithm based on their extreme value point adjust itself.Applied research is mainly widely applied to various fields about the advantagesof particle swarm optimization (PSO) algorithm. But it also overcomes the defect ofthe algorithm itself, introducing some technology in particle swarm optimization (PSO)algorithm. particle swarm algorithm can solve some practical problems, this algorithmgreatly reduces the original time of consumption.The gray, in simple terms, is the color image into a gray image so that it appliesfor the subsequent processing. Generally the gray processing accuracy is higher, so thecolor image into the gray scale is necessary, and it is mainly for computer processingconvenience.In order to assist the medical treatment, it need to deal with the optimization ofthe images into intuitive Numbers and need the segment the image and overlap theoriginal image. Then get the gray-level segmentation points remaining.Firstly, we get the gray image. Then, we use Particle Swarm Optimization toseparate the gray image so that we can get the best threshold. After these steps, we getthe picture after segmenting. We make the original picture coincide with the segmentedone,getting the gray of the remaining points, Then start the data analysis, getting themax，min，average data.After analysis, we can easily find that the remaining points concentrate in thethree bone vortexes, what conform to the truth.Compared with the ATO and BP-GA optimization, we can make sure that theparticle swarm optimization have the correctness and efficiency.