Detection of Curvilinear Structure in Images by a Multi-Centered Hough Forest Method

Automatic detection of curvilinear structure in images is a challenging task in computer vision applications. In this paper, we present a novel supervised learning method to identify curvilinear structure based on a modified Hough forest framework. We regard the curvilinear structure as a special object which has multiple object centers at centerline points, and construct a multi-centered Hough forest (MCHF) to cast confidence votes for each image point being a local curvilinear center in generalized Hough space. Considering the specific properties of curvilinear structure, we modify the classical Hough forest method in three aspects. First, the features are composed of a set of filter responses, and each filter is a base which is learned by sparse presentation techniques. Second, the offset of an image patch against its reference center is represented by a displacement function instead of Euclidean distance used in the classical Hough forest. Third, the centerline orientation is incorporated into the forest to reflect the local trend, which is considered in Hough voting. We conduct experiments on retinal vessel, neuron, and aerial road images. Both visualized and quantitative results demonstrate the good performance of MCHF in detecting complex curvilinear structures in various images.