Extraction of arterial and venous trees from disconnected vessel segments in fundus images

The accurate automated extraction of arterial and venous (AV) trees in fundus images subserves investigation into the correlation of global features of the retinal vasculature with retinal abnormalities. The accurate extraction of AV trees also provides the opportunity to analyse the physiology and hemodynamic of blood flow in retinal vessel trees. A number of common diseases, including Diabetic Retinopathy, Cardiovascular and Cerebrovascular diseases, directly affect the morphology of the retinal vasculature. Early detection of these pathologies may prevent vision loss and reduce the risk of other life-threatening diseases. Automated extraction of AV trees requires complete segmentation and accurate classification of retinal vessels. Unfortunately, the available segmentation techniques are susceptible to a number of complications including vessel contrast, fuzzy edges, variable image quality, media opacities, and vessel overlaps. Due to these sources of errors, the available segmentation techniques produce partially segmented vascular networks. Thus, extracting AV trees by accurately connecting and classifying the disconnected segments is extremely complex. This thesis provides a novel graph-based technique for accurate extraction of AV trees from a network of disconnected and unclassified vessel segments in fundus viii images. The proposed technique performs three major tasks: junction identification, local configuration, and global configuration. A probabilistic approach is adopted that rigorously identifies junctions by examining the mutual associations of segment ends. These associations are determined by dynamically specifying regions at both ends of all segments. A supervised Naïve Bayes inference model is developed that estimates the probability of each possible configuration at a junction. The system enumerates all possible configurations and estimates posterior probability of each configuration. The likelihood function estimates the conditional probability of the configuration using the statistical parameters of distribution of colour and geometrical features of joints. The parameters of feature distributions and priors of configuration are obtained through supervised learning phases. A second Naïve Bayes classifier estimates class probabilities of each vessel segment 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 their possible connective associations. The unary and pairwise potentials for ST-graph are estimated using the class and configuration probabilities obtained earlier. This translates the classification and configuration problems into a general binary labelling graph problem. The ST-graph is interpreted as a flow network for energy minimization a minimum ST-graph cut is obtained using the Ford-Fulkerson algorithm, from which the estimated AV trees are extracted. The performance is evaluated by implementing the system on test images of DRIVE dataset and comparing the obtained results with the ground truth data. The ground truth data is obtained by establishing a new dataset for DRIVE images with manually classified vessels. The system outperformed benchmark methods and produced excellent results

A Bayesian framework for the local configuration of retinal junctions

Retinal images contain forests of mutually intersecting and overlapping venous and arterial vascular trees. The geometry of these trees shows adaptation to vascular diseases including diabetes, stroke and hypertension. Segmentation of the retinal vascular network is complicated by inconsistent vessel contrast, fuzzy edges, variable image quality, media opacities, complex intersections and overlaps. This paper presents a Bayesian approach to resolving the con- figuration of vascular junctions to correctly construct the vascular trees. A probabilistic model of vascular joints (terminals, bridges and bifurcations) and their configuration in junctions is built, and Maximum A Posteriori (MAP) estimation used to select most likely configurations. The model is built using a reference set of 3010 joints extracted from the DRIVE public domain vascular segmentation dataset, and evaluated on 3435 joints from the DRIVE test set, demonstrating an accuracy of 95.2%

A probabilistic model for the optimal configuration of retinal junctions using theoretically proven features

This paper aims to reconstruct retinal vessel trees from the broken vessel segments in fund us images for clinical studies and early diagnosis of systemic diseases including diabetic retinopathy, atherosclerosis, and hypertension. A Naive Bayes model is proposed for correct configurations of segments at retinal junctions including bifurcations, crossovers, overlaps, and mixture of these. The Maximum A Posteriori (MAP) is established to select the most likely configuration. In addition, the feature set consists of proportional associations of vessels width, angle and orientation. These theoretically proven associations are based on the optimality principles of minimum work in the vasculature for blood flow efficiency. We modelled the system using the training set of DRIVE database, tested on the testing set of same database, and produced 93.3 overall accuracy

Automatic localization of the optic disc in retinal fundus images using multiple features

Accurate optic disc localization is an essential step for a reliable retinal screening system. Existing methods for the optic disc localization may fail when encountering distractors such as imprecise boundaries, deceptive edge features and inconsistent contrast in retinal images. This paper presents an algorithm (Multi-Scheme method) for localization of the optic disc. The algorithm involves prior domain knowledge such as the optic disc size, cup-to-disc ratio (CDR) and vessel convergence feature to evaluate the confidence level for the candidate region(s) at each thresholding level. Based on the confidence level, the algorithm heuristically decides whether or not to opt for multi-scheme policy for a given image. For optimization, the Computed Response (CR) from variant versions of the same image is calculated in parallel and fits a contour to the optic disc through an iterative process of updating the location of the centre of the contour. The proposed approach has been validated using dataset ONHSD [3] and diaretdb0 [16]; and the results show the robustness and reliability of the proposed method even in the presence of distractors

A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. An obvious concern regarding the strength of these methodologies is the closeness of the result of a particular method to the gold standard. Unfortunately, the research community lacks benchmarks, resulting in increased subjective error, biased opinion and an uncertain progress. This paper introduces a manually-labeled, artery/vein categorized gold standard image database, as an extension of the most widely used image set DRIVE. The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty. A two-step validation phase consists of verification from the trained computer vision observers and a professional ophthalmologist, followed by a comparison with a gold standard set for the junction locations introduced in V4-Like filters. Our gold standard is in highly reliable form; offers research community for the result comparison and progress evaluation. © 2013 IEEE.</p

Automatic localization of the optic disc in retinal fundus images using multiple features

Accurate optic disc localization is an essential step for a reliable retinal screening system. Existing methods for the optic disc localization may fail when encountering distractors such as imprecise boundaries, deceptive edge features and inconsistent contrast in retinal images. This paper presents an algorithm (Multi-Scheme method) for localization of the optic disc. The algorithm involves prior domain knowledge such as the optic disc size, cup-to-disc ratio (CDR) and vessel convergence feature to evaluate the confidence level for the candidate region(s) at each thresholding level. Based on the confidence level, the algorithm heuristically decides whether or not to opt for multi-scheme policy for a given image. For optimization, the Computed Response (CR) from variant versions of the same image is calculated in parallel and fits a contour to the optic disc through an iterative process of updating the location of the centre of the contour. The proposed approach has been validated using dataset ONHSD [3] and diaretdb0 [16]; and the results show the robustness and reliability of the proposed method even in the presence of distractors.</p

Segmentation of Pancreatic Subregions in Computed Tomography Images

The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation—one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established