Extraction of arterial and venous trees from disconnected vessel segments in fundus images
Creators: name: Qureshi, Touseef Ahmad
Publisher: name: University of Lincoln
Year: 2023
Subjects: F350 - Medical physics

description: The accurate automated extraction of arterial and venous (AV) trees in fundus imagessubserves investigation into the correlation of global features of the retinal vasculaturewith retinal abnormalities. The accurate extraction of AV trees also providesthe opportunity to analyse the physiology and hemodynamic of blood flow in retinalvessel trees. A number of common diseases, including Diabetic Retinopathy, Cardiovascularand Cerebrovascular diseases, directly affect the morphology of the retinalvasculature. Early detection of these pathologies may prevent vision loss and reducethe risk of other life-threatening diseases.Automated extraction of AV trees requires complete segmentation and accurateclassification of retinal vessels. Unfortunately, the available segmentation techniquesare susceptible to a number of complications including vessel contrast, fuzzy edges,variable image quality, media opacities, and vessel overlaps. Due to these sources oferrors, the available segmentation techniques produce partially segmented vascularnetworks. Thus, extracting AV trees by accurately connecting and classifying thedisconnected segments is extremely complex.This thesis provides a novel graph-based technique for accurate extraction of AVtrees from a network of disconnected and unclassified vessel segments in fundusviiiimages. The proposed technique performs three major tasks: junction identification,local configuration, and global configuration.A probabilistic approach is adopted that rigorously identifies junctions by examiningthe mutual associations of segment ends. These associations are determined bydynamically specifying regions at both ends of all segments. A supervised NaïveBayes inference model is developed that estimates the probability of each possibleconfiguration at a junction. The system enumerates all possible configurations andestimates posterior probability of each configuration. The likelihood function estimatesthe conditional probability of the configuration using the statistical parametersof distribution of colour and geometrical features of joints. The parameters of featuredistributions and priors of configuration are obtained through supervised learningphases. A second Naïve Bayes classifier estimates class probabilities of each vesselsegment utilizing colour and spatial properties of segments.The global configuration works by translating the segment network into an STgraph(a specialized form of dependency graph) representing the segments and theirpossible connective associations. The unary and pairwise potentials for ST-graphare estimated using the class and configuration probabilities obtained earlier. Thistranslates the classification and configuration problems into a general binary labellinggraph problem. The ST-graph is interpreted as a flow network for energy minimizationa minimum ST-graph cut is obtained using the Ford-Fulkerson algorithm, fromwhich the estimated AV trees are extracted.The performance is evaluated by implementing the system on test images ofDRIVE dataset and comparing the obtained results with the ground truth data. Theground truth data is obtained by establishing a new dataset for DRIVE images withmanually classified vessels. The system outperformed benchmark methods andproduced excellent results.