An intensive restraint topology adaptive snake model and its application in tracking dynamic image sequence

The aim of this work is to propose an improved intensive restraint topology adaptive snake model for detecting and tracking target contours with complex topological morphology from dynamic gray-scale image sequences. The image is discretized by orthogonal equidistant grid lines and snake nodes are constrained to move only from one grid vertex to another along grid lines. Thus, the computation is much simpler than those of other T-snakes since complicated calculation of intersection points of snake curve and grid lines is not required. Topological transformation is implemented through splitting snake nodes as well as automatically detecting and eliminating topological collision. An adaptive weighted expansion energy term is introduced into snake’s energy function driving the model to pass across the region with non-uniform intensity distribution inside the target region. The required manual interaction is reduced to only select a random point within the target region in the first frame of the image sequence. For subsequent frames, the detection result in the previous frame is regarded as the initial model in the current frame. Through experiments on simulated image data and clinical X-ray coronary angiographic sequences, the model’s topological flexibility and lower computation cost compared with other T-snakes are demonstrated. Also, the sensitivity of the model to weights of the energy terms and grid size as well as the initial plan of the model is discussed based on experimental results.