Fast marching method is an important approach for boundary detection in medical images. However, it is difficult to be applied into echocardiographic images for the inevitable noise and artifacts. This paper presents an improved fast marching approach for boundary detection of echocardiographic images, and validates this approach by detecting and tracking the endocardial boundary in echocardiographic images. Firstly, the traditional fast marching algorithm is applied to the echocardiographic images and the existing problems of the traditional fast marching algorithm are given. Then, the algorithm is improved by introducing the advancing front’s average energy into the speed term, instead of determining the speed term only with the local image features. The experimental results show that the improved algorithm is very effective and robust.
This paper explores a novel dynamic programming (DP) based optimal technique in ultrasound image (USI) edge detection, which is less constrained now than previous. Dynamic programming is an optimal approach in multistage decision-making. In an image segmentation system, we want to find a global optimal contour with connectedness and closeness. The DP algorithms process the object image to get the minimum cumulative cost matrix to tracing a global optimal edge. Combined with LUM nonlinear enhancement filter and Gaussian preprocessor, this method shows robustness on noisy image edge detection.
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