Extending Bayesian network models for mining and classification of glaucoma

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process

En face OCT imaging for the assessment of glaucoma

Glaucoma is a leading cause of irreversible vision loss globally, and demands early and accurate diagnosis. OCT has become a key investigative technique in glaucoma, and, although it provides invaluable clinical support, detection of early glaucoma remains imperfect. Recent OCT developments enabled direct assessment of retinal nerve fibre bundle (RNFB) reflectance in en face OCT images. The technique has considerable potential in the assessment of glaucoma, yet it has limited clinical usability due to an incomplete understanding of RNFB features in healthy and glaucoma eyes and the lack of accepted methods to identify reflectance defects. This thesis aimed to better understand characteristics of RNFB reflectance in en face OCT imaging and to develop objective methods to extract defects in this domain. Structural and functional measures of glaucoma changes were collected in eyes with established glaucoma and age-similar controls. Results showed that the healthy configuration of RNFB varies across the retina and between different eyes. We developed a method for automated and objective examination of reflectivity changes in en face images. This method considers individual anatomy and varying RNFB configuration, and found more abnormalities than previous approaches. Measures of en face reflectance and conventional retinal nerve fibre layer thickness were strongly related. The agreement between changes of reflectance and visual function was moderate-to-good, and both testing domains presented concordant abnormalities in all tested eyes. Following further minimisation of artefacts in en face images, direct use of reflectance analysis or its combination with perimetry appear viable and with significant potential for clinical examination of glaucoma

A novel approach for multimodal graph dimensionality reduction

This thesis deals with the problem of multimodal dimensionality reduction (DR), which arises when the input objects, to be mapped on a low-dimensional space, consist of multiple vectorial representations, instead of a single one. Herein, the problem is addressed in two alternative manners. One is based on the traditional notion of modality fusion, but using a novel approach to determine the fusion weights. In order to optimally fuse the modalities, the known graph embedding DR framework is extended to multiple modalities by considering a weighted sum of the involved affinity matrices. The weights of the sum are automatically calculated by minimizing an introduced notion of inconsistency of the resulting multimodal affinity matrix. The other manner for dealing with the problem is an approach to consider all modalities simultaneously, without fusing them, which has the advantage of minimal information loss due to fusion. In order to avoid fusion, the problem is viewed as a multi-objective optimization problem. The multiple objective functions are defined based on graph representations of the data, so that their individual minimization leads to dimensionality reduction for each modality separately. The aim is to combine the multiple modalities without the need to assign importance weights to them, or at least postpone such an assignment as a last step. The proposed approaches were experimentally tested in mapping multimedia data on low-dimensional spaces for purposes of visualization, classification and clustering. The no-fusion approach, namely Multi-objective DR, was able to discover mappings revealing the structure of all modalities simultaneously, which cannot be discovered by weight-based fusion methods. However, it results in a set of optimal trade-offs, from which one needs to be selected, which is not trivial. The optimal-fusion approach, namely Multimodal Graph Embedding DR, is able to easily extend unimodal DR methods to multiple modalities, but depends on the limitations of the unimodal DR method used. Both the no-fusion and the optimal-fusion approaches were compared to state-of-the-art multimodal dimensionality reduction methods and the comparison showed performance improvement in visualization, classification and clustering tasks. The proposed approaches were also evaluated for different types of problems and data, in two diverse application fields, a visual-accessibility-enhanced search engine and a visualization tool for mobile network security data. The results verified their applicability in different domains and suggested promising directions for future advancements.Open Acces

Predictive trend mining for social network analysis

This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established