Li Hongsheng, Shen Tian, Huang Xiaolei
Department of Computer Science & Engineering, Lehigh University, USA.
Inf Process Med Imaging. 2011;22:411-23. doi: 10.1007/978-3-642-22092-0_34.
We introduce a novel algorithm for actin filament segmentation in 2D TIRFM image sequences. This problem is difficult because actin filaments dynamically change shapes during their growth, and the TIRFM images are usually noisy. We ask a user to specify the two tips of a filament of interest in the first frame. We then model the segmentation problem in an image sequence as a temporal chain, where its states are tip locations; given candidate tip locations, actin filaments' body points are inferred by a dynamic programming method, which adaptively generates candidate solutions. Combining candidate tip locations and their inferred body points, the temporal chain model is efficiently optimized using another dynamic programming method. Evaluation on noisy TIRFM image sequences demonstrates the accuracy and robustness of this approach.
我们介绍了一种用于二维全内反射荧光显微镜（TIRFM）图像序列中肌动蛋白丝分割的新算法。这个问题具有挑战性，因为肌动蛋白丝在生长过程中会动态改变形状，并且TIRFM图像通常存在噪声。我们要求用户在第一帧中指定感兴趣的细丝的两个尖端。然后，我们将图像序列中的分割问题建模为一个时间链，其状态是尖端位置；给定候选尖端位置，通过动态规划方法推断肌动蛋白丝的主体点，该方法自适应地生成候选解决方案。结合候选尖端位置及其推断的主体点，使用另一种动态规划方法对时间链模型进行有效优化。对有噪声的TIRFM图像序列的评估证明了该方法的准确性和鲁棒性。
