Image mining approaches for the screening of age-related macular degeneration

Age-related Macular Degeneration (AMD) is the most common cause of irreversible vision loss in those aged over 50. In this chapter we investigating two techniques to support automated AMD screening. First of all we conceptualise AMD screening in terms of a binary classification problem (disease v. no-disease). We then propose and compare two very different techniques whereby the desired classification can be undertaken. The first is founded on a histogram based retinal image representation and the second on a graph based representation. In the histogram based approach each image is defined in terms of a histograms that in turn is presented as "time series curves". Given a training set (a collection of labelled positive and negative examples) we create a Case Base (CB) of labelled curves to which a Case Based Reasoning (CBR) mechanism is applied so as to classify "unseen" images according to whether they feature AMD or not. Curve comparison is conducted using a time series comparison technique. For the graph mining based approach a hierarchical decomposition technique is proposed, whereby pre-labelled retinal images contained in a training set, are successively decomposed into smaller and smaller segments until each segment describes a uniform set of features. The resulting decomposition is stored in a tree structure, one per image, to which a frequent sub-graph (sub-tree) mining technique is applied so as to identify the frequently occurring sub-trees that exist within the overall tree data set. The identified sub-trees then form the global attribute set from which a collection of feature vectors (one per image) is derived so as to describe the training set. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The two approaches are compared and evaluated using two publicly available data sets, ARIA and STARE, respectively comprising 161 and 97 pre-labelled retinal images. The paper details both approaches and reports on their evaluation

Convolutional Neural Networks for Diabetic Retinopathy

The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task. On the dat

White matter hyperintensity volume and post-stroke cognition: an individual patient data pooled analysis of nine ischemic stroke cohort studies

Background and aims: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet uncertainty remains about affected domains, the role of other pre-existing brain injury, and infarct-types in the relation between WMH burden and post-stroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. Methods We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available MRI and multi-domain cognitive assessment <15 months post73 stroke. Linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (z-scores; attention & executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct-type. Pre-existing brain injury was accounted for in the multivariable models and all analyses were corrected for study site as a random effect.. Results In the total sample (67 years (SD 11.5), 40% female), we found a dose-dependent inverse relationship between WMH volume and post-stroke cognitive functioning across all four cognitive domains (coefficients ranging from -0.09 (SE 0.04, p=0.01) for verbal memory to -0.19 (SE 0.03, p<0.001) for attention & executive functioning). This relation was independent of acute infarct volume and presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain86 specific functioning was also largely independent of infarct-type. Conclusion: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct-type