Research On Key Technologies Of Medical Image Processing Based On EGEE

The medical image processing and analysis has improved the level of diagnosis and treat greatly and has been paid more attention to by people with the help of technologies of computer image processing and analysis, computer graphics, virtual reality, computer network and so on. It has become a research and application hotspot at home and abroad, and former a new crossover subject with rapid development.With the development of the medical imaging technology, the accuracy of medical images is higher and higher, data volume is larger and larger at the same time. Therefore, medical image processing would require a handling environment which has a large storage capacity and a strong computing capable. The EGEE(The Enabling Grids for E-sciencE) just to meet this demand. So the study of medical image processing technology which based on EGEE has great significance.This thesis includes contents of three main aspects:Medical image processing platform was designed and developed based on ITK, VTK and QT. It integrated image filtering, segmentation and three-dimensional visualization algorithm and implements a full-featured, interface rich, scalable platform for medical image processing, Can be used as a basis for algorithm development platform for R & D.In order to solve the difficult of determine the growth rules in conventional regional growth algorithm and the slowly of support vector machine segmentation algorithm, an image segmentation method combined support vector machine and regional growth was proposed. Firstly, select a certain numbers of sample point from target area and non-target area and train the support vector machine classification, then use the trained classification search seed point and regional growing, the support vector machine classification is used as growth rules, the last, some necessary retrogressing were used for the edge and noise. The experimental results show that this algorithm is feasible and it performs better than conventional region growth segmentation algorithm and faster then conventional support vector machine segmentation algorithm.In the medical image 3D reconstruction, proposed a 3D reconstruction parallel strategy based on EGEE. First, the task is divided into a number of copies and distributed to the sub-processes; secondly, each sub-process using MC algorithm extraction isosurface and linked triangulated surfaces into Triangle and back the results to the main process; finally, the main process will be merge all results of sub-processes returned and displayed. Experimental tests show that the 3D reconstruction parallel method can effectively improve the speed of three-dimensional reconstruction and rebuilding good effect.