Automated detection of proliferative diabetic retinopathy from retinal images

Diabetic retinopathy (DR) is a retinal vascular disease associated with diabetes and it is one of the most common causes of blindness worldwide. Diabetic patients regularly attend retinal screening in which digital retinal images are captured. These images undergo thorough analysis by trained individuals, which can be a very time consuming and costly task due to the large diabetic population. Therefore, this is a field that would greatly benefit from the introduction of automated detection systems. This project aims to automatically detect proliferative diabetic retinopathy (PDR), which is the most advanced stage of the disease and poses a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. Their tortuous, convoluted and obscure appearance can make them difficult to detect. In this thesis, we present a methodology based on the novel approach of creating two different segmented vessel maps. Segmentation methods include a standard line operator approach and a novel modified line operator approach. The former targets the accurate segmentation of new vessels and the latter targets the reduction of false responses to non-vessel edges. Both generated binary vessel maps hold vital information which is processed separately using a dual classification framework. Features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. The proposed methodology, using a dataset of 60 images, achieves a sensitivity of 100.00% and a specificity of 92.50% on a per image basis and a sensitivity of 87.93% and a specificity of 94.40% on a per patch basis. The thesis also presents an investigation into the search for the most suitable features for the classification of PDR. This entails the expansion of the feature vector, followed by feature selection using a genetic algorithm based approach. This provides an improvement in results, which now stand at a sensitivity and specificity 3 of 100.00% and 97.50% respectively on a per image basis and 91.38% and 96.00% respectively on a per patch basis. A final extension to the project sees the framework of dual classification further explored, by comparing the results of dual SVM classification with dual ensemble classification. The results of the dual ensemble approach are deemed inferior, achieving a sensitivity and specificity of 100.00% and 95.00% respectively on a per image basis and 81.03% and 95.20% respectively on a per patch basis

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Retinal Vascular Tortuosity and Diameter Associations with Adiposity and Components of Body Composition.

OBJECTIVE: The aim of this study was to assess whether adiposity or body composition relates to microvascular characteristics of the retina, indicative of cardiometabolic function. METHODS: A fully automated QUARTZ software processed retinal images from 68,550 UK Biobank participants (aged 40-69 years). Differences in retinal vessel diameter and tortuosity with body composition measures from the Tanita analyzer were obtained by using multilevel regression analyses adjusted for age, sex, ethnicity, clinic, smoking, and Townsend deprivation index. RESULTS: Venular tortuosity and diameter increased by approximately 2% (P < 10-300 ) and 0.6 μm (P < 10-6 ), respectively, per SD increase in BMI, waist circumference index, waist-hip ratio, total body fat mass index, and fat-free mass index (FFMI). Venular associations with adiposity persisted after adjustment for FFMI, whereas associations with FFMI were weakened by FMI adjustment. Arteriolar diameter (not tortuosity) narrowing with FFMI was independent of adiposity (-0.6 μm; -0.7 to -0.4 μm per SD increment of FFMI), while adiposity associations with arteriolar diameter were largely nonsignificant after adjustment for FFMI. CONCLUSIONS: This demonstrates, on an unprecedented scale, that venular tortuosity and diameter are more strongly associated with adiposity, whereas arteriolar diameter relates more strongly to fat-free mass. Different attributes of the retinal microvasculature may reflect distinct roles of body composition and fatness on the cardiometabolic system

Robust non-intrusive load monitoring (NILM) with unknown loads

A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households

A real-time non-intrusive load monitoring system

A complete real-time (RT) implementation of a NonIntrusive Load Monitoring (NILM) system based on uncorrelated spectral components of the active power consumption signal is presented. Unlike existing NILM techniques that rely on multiple measurements taken at high sampling rates and, yet only proven in simulated environments, this proposed RT-NILM solution yield accurate results even with a single active power measurement taken at a low sampling rate from real-time hardware. An Active Power Meter (APM) was developed and constructed, then, used with the designed MATLAB ™ Graphical User Interface (GUI) to break down the acquired active power signal of an appliance into subspace components (SCs) so as to construct a unique information rich appliance signature via the Karhunen Love expansion (KLE). Using the same GUI, signatures for all possible device combinations were constructed to form the appliance signature database. Then, a separate GUI was designed to identify the turned-on appliance combination in the current time window after reading the total power consumption of a device combination via the constructed APM. There in the identification process, SC level power conditions were used to reduce the number of possible appliance combinations rapidly before applying the maximum a posteriori estimation. The proposed RT-NILM implementation was validated by feeding the data in real-time from a laboratory arrangement consisting of ten household appliances