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No description available.

No description available.

No description available.

No description available.

Heart disease is a major cause of death in western countries. In order to diagnose and monitor heart disease, 3D echocardiography is an important tool, as it provides a fast, relatively low-cost, portable and harmless way of imaging the moving heart. Segmentation of cardiac walls is an indispensable method of obtaining quantitative measures of heart function. However segmentation of ultrasound images has its challenges: image quality is often relatively low and current segmentation methods are often not fast. It is desirable to make the segmentation technique as fast as possible, making quantitative heart function measures available at the time of recording. In this thesis, we test two state-of-the-art fast segmentation techniques to address this issue; furthermore, we develop a novel technique for finding the best segmentation propagation strategy between points of time in a cardiac image sequence. The first fast method is Graph Cuts (GC), an energy minimisation technique that represents the image as a graph. We test this method on static 3D echocardiography to segment the myocardium, varying the importance of the regulariser function. We look at edge measures, position constraints and tissue characterisation and find that GC is relatively fast and accurate. The second fast method is Random Forests (RFos), a discriminative classifier using binary decision trees, used in machine learning. To our knowledge, we are the first to test this method for myocardial segmentation on 2D and 3D static echocardiography. We investigate the number of trees, image features used, some internal parameters, and compare with intensity thresholding. We conclude that RFos are very fast and more accurate than GC segmentation. The static RFo method is subsequently applied to all time frames. We describe a novel optical flow based propagation technique that improves the static results by propagating the results from well-performing time frames to less-performing frames. We describe a learning algorithm that learns for each frame which propagation strategy is best. Furthermore, we look at the influence of the number of images and of the training set available per tree, and we compare against other methods that use motion information. Finally, we perform the same propagation learning method on the static GC results, concluding that the propagation method improves the static results in this case as well. We compare the dynamic GC results with the dynamic RFo results and find that RFos are more accurate and faster than GC.