González German, Aguet François, Fleuret François, Unser Michael, Fua Pascal
Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
Med Image Comput Comput Assist Interv. 2009;12(Pt 2):625-32. doi: 10.1007/978-3-642-04271-3_76.
Most state-of-the-art algorithms for filament detection in 3-D image-stacks rely on computing the Hessian matrix around individual pixels and labeling these pixels according to its eigenvalues. This approach, while very effective for clean data in which linear structures are nearly cylindrical, loses its effectiveness in the presence of noisy data and irregular structures. In this paper, we show that using steerable filters to create rotationally invariant features that include higher-order derivatives and training a classifier based on these features lets us handle such irregular structures. This can be done reliably and at acceptable computational cost and yields better results than state-of-the-art methods.
大多数用于三维图像堆栈中细丝检测的最先进算法依赖于计算单个像素周围的海森矩阵，并根据其特征值对这些像素进行标记。这种方法虽然对于线性结构近似为圆柱形的干净数据非常有效，但在存在噪声数据和不规则结构的情况下会失去其有效性。在本文中，我们表明使用可控滤波器来创建包含高阶导数的旋转不变特征，并基于这些特征训练分类器，使我们能够处理此类不规则结构。这可以可靠地完成，且计算成本可接受，并且比最先进的方法产生更好的结果。
