Research On Technologies Of Segmentation And Motion Analysis In MR Images

There are image segmentation and matching, structure and motion analysis ect, in main research directions of medical image managing. It has important significance of research into medical image segmentation among them. Because of the complexity and variability of medical images, the image definition will be affected by the existing medical equipment techniques. Apart from that, medical image segmentation becomes even more difficult due to regional illegibility, noise, and different intensity. Currently, medical image automatic segmentation draws continuous attention. Meanwhile, researchers begin to focus on exploring interactive segmentation methods. Analyzing from interactive live wire, Intelligent Scissors model, the parameter active contour model, and geometric active contour model, this topic studies the segmentation of the cardiac MR image by adopting deformable models, and optical flow technology theory and its application in cardiac movement estimation.It analyses two methods of interactive image segmentation, that is, methods of live wire and Intelligent Scissors (IS). Then points out their defects of slow calculating speed and complicated operating while image is segmented.When the traditional parametric active contour model (Snake) is applied to segment the image, an initial contour must be set near the boundary of ROI and the model cannot segment deeply concave regions accurately. On the basis of analyzing the Snake model and improved model, the adaptive active contour model based on distance equalization is proposed. This new model, through the average distance between the vertex and the line of its neighborhood dots as the flexure of Snake model, the inflation is defined through from characteristics of image itself, make the model has its adaptive ability. Meanwhile, the concept of distance equalization also has been proposed. The stability and smoothness of the improved model and the management of deformed concave regions have been deeply analyzed. The segmentation experiments demonstrate the effectiveness of improved model for the cardiac MRI.Based on the characteristics of cardiac MR image, the topic proposes a faster segmentation process. A rough segmentation is made by using Song and Chan method to the feature images, which are the result of k-Means cluster to cardiac MR image. Then it is followed by a further segmentation of the initial level set curve that was got by the first step, based on Chan and Vese method. At the same time, improvement of the simplified Mumford-Shah model proposed by Chan and Vese is made to achieve a more global optimization of energy functions and accelerated evolution process, and a better outline of the scanned regions, which combine to improve the calculation speed and segmentation effectiveness. Experiments prove the rapidness and accuracy of the method in its application in segmentation of cardiac MRI.In respect of the ellipse-like outline of the left ventricle, ellipse shape restriction is adopted based on Chan-Vese model to control evolvement of curve. The evolvement curve of level set being regarded as the new forecast of contour location, ellipse restriction is introduced to modify the forecast result. Accordingly, the results of the forecast and modification are taken respectively as new curve of the level set and shape information.