In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states.
Microscopy is a fundamental technology driving new biological discoveries. Today microscopy allows a large number of images to be acquired using, for example, High Throughput Screening (HTS) and 4D imaging. It is essential to be able to interrogate these images and extract quantitative information in an automated fashion. In the context of neurobiology, it is important to automatically quantify the morphology of neurons in terms of neurite number, length, branching and complexity, etc.
The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computeraided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottleneck issue of filament crossovers is commonly encountered, which essentially hinders the ability of existing systems to conduct large-scale drug screening or practical clinical usage.
This paper aims to trace retinal blood vessel trees in fundus images. This task is far from being trivial as the crossover of vessels are commonly encountered in image-based vessel networks. Meanwhile it is
often crucial to separate the vessel tree structures in applications such as diabetic retinopathy analysis. In this work, a novel directed graph based approach is proposed to cast the task as label propagation over directed graphs, such that the graph is to be partitioned into disjoint sub-graphs,
Structural study of retinal blood vessels provides an early indication of diseases such as diabetic
retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks.
In this paper, we consider a novel graph-based approach to address this tracing with crossover problem:
As an early indication of diseases including diabetes, hypertension,and retinopathy of prematurity, structural study of retinal vessels becomes increasingly important. These studies have driven the