Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots. In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually. A literature review on automated drusen detection showed that the works already published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques. This methodology is based on a detection and modelling algorithm to automatically quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the image, which is used to compute the affected areas. To validate the methodology, two software applications, one for semi-automated (MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have taken place. These compared the automated method to the specialists and evaluated its repeatability. The studies were analysed regarding several indicators, which were based on the total affected area and on a pixel-to-pixel analysis. Due to the high variability among the graders involved in the first study, a new evaluation method, the Weighed Matching Analysis, was developed to improve the pixel-to-pixel analysis by using the statistical significance of the observations to differentiate positive and negative pixels. From the results of these studies it was concluded that the methodology proposed is capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image segmentation,image modelling and images comparison, which are also applicable to other image processing fields

MAMIS – A Multi-Agent Medical Information System – Um sistema multi-agente de informação médica

O sector da saúde está actualmente sofrendo uma reestruturação profunda com vista à digitalização da informação médica o que terá consequências óbvias a nível de qualidade e eficiência do funcionamento dos diversos serviços deste sector. No entanto, a adopção de diferentes formatos de dados coloca graves problemas de compatibilidade tornando a informação dificilmente utilizável fora do sistema onde é originada. Por outro lado, as restritas regras de carácter ético que regulam este sector de actividade levam também a que a disponibilização da informação entre diferentes entidades seja efectuada com muitos receios ou mesmo vedada, exceptuando-se normalmente casos em que existam justificações bem fundamentadas. É portanto natural a grande dificuldade ou mesmo a impossibilidade de criação de sistemas centralizados de colecta de informação sobre os pacientes que possam ser partilhados pela comunidade médica devido a um grande número de razões tanto éticas como legais. Face a estas barreiras, e no intuito de criar uma ferramenta que seja uma mais valia neste sector, desenvolveu-se o sistema MAMIS (Multi-Agent Medical Information System), um sistema multi-agente para informação médica que possibilita a pesquisa de informação médica distribuída, não tendo por base uma partilha de bases de dados, mas sim uma negociação da informação, caso-a-caso, entre os diversos agentes.info:eu-repo/semantics/publishedVersio

Sea urchin granuloma

Injuries caused by venomous and poisonous aquatic animals may provoke important morbidity in humans. The phylum Echinoderma include more than 6000 species of starfish, sea urchins, sand dollars, and sea cucumbers some of which have been found responsible for injuries to humans. Initial injuries by sea urchins are associated with trauma and envenomation, but later effects can be observed. Sea urchin granuloma is a chronic granulomatous skin disease caused by frequent and successive penetration of sea urchin spines which have not been removed from wounds. The authors report a typical case of sea urchin granuloma in a fisherman and its therapeutic implications.Os acidentes por animais aquáticos traumatizantes e venenosos podem provocar morbidez importante em humanos. Equinodermos marinhos incluem mais de 6000 espécies de estrelas-do-mar, ouriços-do-mar, "bolachas-de-praia" e pepinos-do-mar. Vários equinodermos têm sido responsabilizados por acidentes em humanos. Granulomas por ouriço-do-mar são lesões de caráter granulomatoso, crônicas, causada por acidentes com espículas de ouriço-do-mar. Os autores relatam um caso típico de granulomas por ouriço-do-mar ocorrido em um pescador e enfatizam as implicações terapêuticas aplicadas

ANÁLISIS DEL FACTOR DE REDUCCIÓN SÍSMICA, EFECTOS EN EL DESEMPEÑO SÍSMICO Y PROPUESTA DE VALORES REFINADOS PARA EDIFICIOS DUALES DE 5 A 8 NIVELES EN LA CIUDAD DE AREQUIPA

ESTADO ACTUAL DEL DISEÑO SÍSMICO EN NUESTRO PAÍS Y EL EXTRANJERO EL DESEMPEÑO SÍSMICO APLICANDO LA TÉCNICA DEL PUSHOVER L FACTOR DE REDUCCIÓN SÍSMICA VALIDACIÓN DE MODELOS MATEMÁTICOS PARA INVESTIGACIÓN ANÁLISIS DE LOS MODELOS ESTRUCTURALES EN ESTUDIO ANÁLISIS E INTERPRETACIÓN DE RESULTADOS OBTENIDOS ANÁLISIS DE UN EDIFICIO UBICADO EN LA CIUDAD DE AREQUIPA UTILIZANDO EL FACTOR DE REDUCCIÓN PROPUESTO EN EL REGLAMENTO VIGENTE Y LA PROPUESTA DE LA INVESTIGACIÓ

Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.publishersversionpublishe

How to build a 2d and 3d aerial multispectral map?—all steps deeply explained

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015 UIDB/00066/2020The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.publishersversionpublishe

Data fusion approach for eucalyptus trees identification

UIDB/00066/2020 DSAIPA/AI/0100/2018Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through multispectral images, that allow their remote analysis and classification. Analysing those images with data fusion techniques is a promising approach for identification and classification of forest types. Fusion techniques can aggregate various sources of heterogeneous information to generate value-added maps, facilitating forest-type classification. This work applies a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques, to automatically distinguish Eucalyptus trees from satellite images. The algorithm customization was performed with a Portuguese area planted with Eucalyptus. After customizing and validating the approach with several representative scenarios to assess its suitability for automatic classification of Eucalyptus, we tested on a large tile obtaining a sensitivity of 69.61%, with a specificity of 99.43%, and an overall accuracy of 98.19%. This work demonstrates the potential of our approach to automatically classify specific forest types from satellite images, since this is a novel approach dedicated to the identification of eucalyptus trees.publishersversionpublishe

An integrated decision support system for improving wildfire suppression management

Funding Information: This work was financially supported by FCT (National Foundation of Science and Technology) within the Research Unit CTS?Centre of Technology and Systems, UIDB/00066/2020, and the Project forester (PCIF/SSI/0102/2017). We would like to thank the authorities from the municipality of Ma??o, in particular to Engineer Ant?nio Louro, for the valuable support in establishing the user requirements and the feedback for the system?s validation. Special thanks to the Adjunct of National Operations in the National Command of Security Operations (CNOS) part of the National Authority of Civil Protection (ANPC) Alexandre Penha, for their input in the early stages of this work.Wildfires are expected to increase in number, extent, and severity due to climate change. Hence, it is ever more important to integrate technological developments and scientific knowledge into fire management aiming at protecting lives, infrastructure, and the environment. In this paper, a decision support system (DSS) adapted to the Portuguese context and based on multi-sensor technologies and geographic information system (GIS) functionalities is proposed to leverage operational data, enabling faster and more informed decisions to reduce the impact of wildfires. Here we present a flexible and reconfigurable DSS composed of three components: an ArcGIS online feature service that provides operational data and enables a collaborative environment of users that share operational data in near real-time; a mobile client application to interact with the system, enabling the use of GIS technology and visualization dashboards; and a multi-sensor device that collects field data providing value to external services. The design and validation of this system benefitted from the feedback of wildfire management specialists and a partnership with an end-user in the municipality of Mação that also helped establish the system requirements. The validation results demonstrated that a robust system was achieved with fully interoperable components that fulfill the defined system requirements.publishersversionpublishe

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UID/EEA/00066/2019 UIDB/00239/2020The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.publishersversionpublishe

Fully automated countrywide monitoring of fuel break maintenance operations

PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UIDB/00239/2020 UIDB/00066/2020Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.publishersversionpublishe