Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems, Computation, Automation and Networking http://icscan.in

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

Modélisation statistique des structures anatomiques de la rétine à partir d'images de fond d'oeil

L’examen non-invasif du fond d’oeil permet d’identifier sur la rétine les signes de nombreuses pathologies oculaires qui développent de graves symptômes pour le patient pouvant entraîner la cécité. Le réseau vasculaire rétinien peut de surcroît présenter des signes précurseurs de pathologies cardiovasculaires et cérébro-vasculaires. La rétine, où apparaissent ces pathologies, est constituée de plusieurs structures anatomiques dont la variabilité est importante au sein d’une population saine. Pour autant, les évaluations cliniques actuelles ne prennent pas en compte cette variabilité ce qui ne permet pas de détecter précocement ces pathologies. Ces évaluations se basent sur un ensemble restreint de mesures prélevées à partir de structures dont la segmentation manuelle est réalisable par les experts. De plus, elles sont basées sur un seuillage empirique déterminé par les cliniciens et appliqué sur chacune des mesures afin d’établir un diagnostic. Ainsi, les évaluations cliniques actuelles sont affectées par la grande variabilité des structures anatomiques de la rétine au sein de la population et elles n’évaluent pas les anomalies trop difficiles à mesurer manuellement. Dans ce contexte, il convient de proposer de nouvelles mesures cliniques qui tiennent compte de la variabilité normale à l’aide d’une modélisation statistique des structures anatomiques de la rétine. Cette modélisation statistique permet de mieux comprendre et identifier ce qui est normal et comment l’anatomie et ses attributs varient au sein d’une population saine. Cela permet ainsi d’identifier la présence de pathologies à l’aide de nouvelles mesures cliniques construites en tenant compte de la variabilité des attributs de l’anatomie. La modélisation statistique des structures anatomiques de la rétine est cependant difficile étant donné les variations morphologiques et topologiques de ces structures. Les changements morphologiques et topologiques du réseau vasculaire rétinien compliquent son analyse statistique ainsi que les outils de recalage, de segmentation et de représentation sémantique s’y appliquant. Les questions de recherches adressées dans cette thèse sont la production d’outils capables d’analyser la variabilité des structures anatomiques de la rétine et l’élaboration de nouvelles mesures cliniques tenant compte de la variabilité normale de ces structures. Pour répondre à ces questions de recherche, trois objectifs de recherche sont formulés. ----------ABSTRACT: Non-invasive retinal fundus examination allows clinicians to identify signs of many ocular conditions that develop critical symptoms affecting the patient and even leading to blindness. In addition, the retinal vascular network may present early signs of cardiovascular and cerebrovascular diseases. The retina, where these pathologies appear, is composed of several anatomical structures whose variability is considerable within a healthy population. Yet, current clinical evaluations do not take into account this variability, and this does not allow early detection of these pathologies. These evaluations are based on a limited set of measurements taken from structures whose manual segmentation is achievable by the experts. In addition, they are based on empirical thresholding determined by the clinicians and applied to each of the measurements to establish a diagnosis. Thus, current clinical assessments are affected by the large variability of anatomical structures of the retina within a healthy population and do not evaluate abnormalities that are too difficult to measure manually. In this context, it is advisable to propose new clinical measurements that take into account the normal variability using statistical modeling of the anatomical structures of the retina. Such a statistical modeling approach helps us to better understand and identify what is normal and how the anatomy and its attributes vary across a healthy population. This makes it possible to identify the presence of pathologies using new clinical measurements constructed by taking into account the variability of the anatomy’s attributes. Statistical modeling of the anatomical structures of the retina is difficult, however, given the morphological and topological variations of these structures. Morphological and topological changes in the retinal vascular network complicate its statistical analysis as well as the registration methods, segmentation and semantic representation applied to it. The research questions proposed in this thesis pertain to creating tools capable of analyzing the variability of the anatomical structures of the retina and proposing new clinical measures that take into account the normal variability of those structures. To answer these research questions, three research objectives are formulated

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization