While many approaches exist for the automated segmentation of retinal vessels in fundus photographs, limited work has focused on the problem of separating the arterial from the venous trees. The few existing approaches that do exist for separating arteries from veins are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to only the very largest vessels. In this work, we propose a new, more global, optimization framework for separating two overlapping trees within medical images and apply this approach for the separation of arteriovenous trees in low-contrast color fundus images. In particular, our approach has two stages. The first stage is to generate a vessel potential connectivity map (VPCM) consisting of vessel segments and the potential connectivity between them. The second stage is to separate the VPCM into multiple anatomical trees using a graph-based meta-heuristic algorithm. Based on a graph model, the algorithm first uses local knowledge and global constraints of the vasculature to generate near-optimal candidate solutions, and then obtains the final solution based on global costs. We test the algorithm on 48 low-contrast fundus images and the promising results suggest its applicability and robustness.

Abstract. While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixelwise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.

The retinal vessel width relationship at vessel branch points in fundus images is an important biomarker of retinal and systemic disease. We propose a fully automatic method to measure the vessel widths at branch points in fundus images. The method is a graph-based method, in which a graph construction method based on electric field theory is applied which specifically deals with complex branching patterns. The vessel centerline image is used as the initial segmentation of the graph. Branching points are detected on the vessel centerline image using a set of detection kernels. Crossing points are distinguished from branch points and excluded. The electric field based graph method is applied to construct the graph. This method is inspired by the non-intersecting force lines in an electric field. At last, the method is further improved to give a consistent vessel width measurement for the whole vessel tree. The algorithm was validated on 100 artery branchings and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurements from two human experts.

The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008)(2009)(2010)(2011)(2012)(2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014-2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters (e.g., modularity, diameter, etc.) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include:(1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject's influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper.

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