Diabetic retinopathy diagnosis through multi-agent approaches

Programa Doutoral em Engenharia BiomédicaDiabetic retinopathy has been revealed as a serious public health problem in occidental world, since it is the most common cause of vision impairment among people of working age. The early diagnosis and an adequate treatment can prevent loss of vision. Thus, a regular screening program to detect diabetic retinopathy in the early stages could be efficient for the prevention of blindness. Due to its characteristics, digital color fundus photographs have been the preferred eye examination method adopted in these programs. Nevertheless, due to the growing incidence of diabetes in population, ophthalmologists have to observe a huge number of images. Therefore, the development of computational tools that can assist the diagnosis is of major importance. Several works have been published in the recent past years for this purpose; but an automatic system for clinical practice has yet to come. In general, these algorithms are used to normalize, segment and extract information from images to be utilized by classifiers which aim to classify the regions of the fundus image. These methods are mostly based on global approaches that cannot be locally adapted to the image properties and therefore, none of them perform as needed because of fundus images complexity. This thesis focuses on the development of new tools based on multi-agent approaches, to assist the diabetic retinopathy early diagnosis. The fundus image automatic segmentation concerning the diabetic retinopathy diagnosis should comprise both pathological (dark and bright lesions) and anatomical features (optic disc, blood vessels and fovea). In that way, systems for the optic disc detection, bright lesions segmentation, blood vessels segmentation and dark lesions segmentation were implemented and, when possible, compared to those approaches already described in literature. Two kinds of agent based systems were investigated and applied to digital color fundus photographs: ant colony system and multi-agent system composed of reactive agents with interaction mechanisms between them. The ant colony system was used to the optic disc detection and for bright lesion segmentation. Multi-agent system models were developed for the blood vessel segmentation and for small dark lesion segmentation. The multi-agent system models created in this study are not image processing techniques on their own, but they are used as tools to improve the traditional algorithms results at the micro level. The results of all the proposed approaches are very promising and reveal that the systems created perform better than other recent methods described in the literature. Therefore, the main scientific contribution of this thesis is to prove that multi-agent systems based approaches can be efficient in segmenting structures in retinal images. Such an approach overcomes the classic image processing algorithms that are limited to macro results and do not consider the local characteristics of images. Hence, multi-agent systems based approaches could be a fundamental tool, responsible for a very efficient system development to be used in screening programs concerning diabetic retinopathy early diagnosis.A retinopatia diabética tem-se revelado como um problema sério de saúde pública no mundo ocidental, uma vez que é a principal causa de cegueira entre as pessoas em idade ativa. Contudo, a perda de visão pode ser prevenida através da deteção precoce da doença e de um tratamento adequado. Por isso, um programa regular de rastreio e monitorização da retinopatia diabética pode ser eficiente na prevenção da deterioração da visão. Devido às suas características, a fotografia digital colorida do fundo do olho tem sido o exame adotado neste tipo de programas. No entanto, devido ao aumento da incidência da diabetes na população, o número de imagens a serem analisadas pelos oftalmologistas é elevado. Assim sendo, é muito importante o desenvolvimento de ferramentas computacionais para auxiliar no diagnóstico desta patologia. Nos últimos anos, têm sido vários os trabalhos publicados com este propósito; porém, não existe ainda um sistema automático (ou recomendável) para ser usado nas práticas clínicas. No geral, estes algoritmos são usados para normalizar, segmentar e extrair informação das imagens que vai ser utilizada por classificadores, cujo objetivo é identificar as regiões da imagem que se procuram. Estes métodos são maioritariamente baseados em abordagens globais que não podem ser localmente adaptadas às propriedades das imagens e, portanto, nenhum apresenta a performance necessária devido à complexidade das imagens do fundo do olho. Esta tese foca-se no desenvolvimento de novas ferramentas computacionais baseadas em sistemas multi-agente, para auxiliar na deteção precoce da retinopatia diabética. A segmentação automática das imagens do fundo do olho com o objetivo de diagnosticar a retinopatia diabética, deve englobar características patológicas (lesões claras e escuras) e anatómicas (disco ótico, vasos sanguíneos e fóvea). Deste modo, foram criados sistemas para a deteção do disco ótico e para a segmentação das lesões claras, dos vasos sanguíneos e das lesões escuras e, quando possível, estes foram comparados com abordagens já descritas na literatura. Dois tipos de sistemas baseados em agentes foram investigados e aplicados nas imagens digitais coloridas do fundo do olho: sistema de colónia de formigas e sistema multi-agente constituído por agentes reativos e com mecanismos de interação entre eles. O sistema de colónia de formigas foi usado para a deteção do disco ótico e para a segmentação das lesões claras. Modelos de sistemas multi-agente foram desenvolvidos para a segmentação dos vasos sanguíneos e das lesões escuras. Os modelos multi-agentes criados ao longo deste estudo não são por si só técnicas de processamento de imagem, mas são sim usados como ferramentas para melhorar os resultados dos algoritmos tradicionais no baixo nível. Os resultados de todas as abordagens propostas são muito promissores e revelam que os sistemas criados apresentam melhor performance que outras abordagens recentes descritas na literatura. Posto isto, a maior contribuição científica desta tese é provar que abordagens baseadas em sistemas multi-agente podem ser eficientes na segmentação de estruturas em imagens da retina. Uma abordagem deste tipo ultrapassa os algoritmos clássicos de processamento de imagem, que se limitam aos resultados de alto nível e não têm em consideração as propriedades locais das imagens. Portanto, as abordagens baseadas em sistemas multi-agente podem ser uma ferramenta fundamental, responsável pelo desenvolvimento de um sistema eficiente para ser usado nos programas de rastreio e monitorização da retinopatia diabética.Work supported by FEDER funds through the "Programa Operacional Factores de Competitividade – COMPETE" and by national funds by FCT- Fundação para a Ciência e a Tecnologia. C. Pereira thanks the FCT for the SFRH / BD / 61829 / 2009 grant

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Toward a Criminal Law for Cyberspace: Distributed Security

Cybercrime creates unique challenges for the reactive model of crime control that has been predominant for approximately the last century and a half. That model makes certain assumptions about crime, which derive from characteristics of real-world crime. These assumptions do not hold for cybercrime, so the reactive model is not an appropriate means of dealing with online crime. The article explains how modified principles of criminal law can be utilized to implement a new, non-reactive model which can deal effectively with cybercrime. This model of distributed security emphasizes prevention, rather than reaction, which is achieved by holding citizens liable for their failure to prevent cybercrime

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Active Materials

What is an active material? This book aims to redefine perceptions of the materials that respond to their environment. Through the theory of the structure and functionality of materials found in nature a scientific approach to active materials is first identified. Further interviews with experts from the natural sciences and humanities then seeks to question and redefine this view of materials to create a new definition of active materials

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp