Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network

As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.Comment: IFAC World Congress 2023 pape

Low-cost IoT design and implementation of a remote food and water control system for pet owners

The Internet of things IoT concept basically aims to connect any device, vehicle or other items to transfer data between these subsystems. IoT applications make people’s lives easier and more efficient in many ways. One of the areas where IoT can be useful is that monitoring food and water supply for pets that are left unattended for either a short or a long time. The main purpose of this paper is to state and detail an instance of low-cost IoT by designing a remote food and water control system for pet owners. The whole system consists of three subsystems; the performing unit, server, and mobile application. Each subsystem has developed by using different open-source programming languages and frameworks

Omnidirectional underwater surveying and telepresence

Exploratory dives are traditionally the first step for marine scientists to acquire information on a previously unknown area of scientific interest. Manned submersibles have been the platform of choice for such exploration, as they allow a high level of environmental perception by the scientist on-board, and the ability to take informed decisions on what to explore next. However, manned submersibles have extremely high operation costs and provide very limited bottom time. Remotely operated vehicles (ROVs) can partially address these two issues, but have operational and cost constraints that restrict their usage. This paper discusses new capabilities to assist scientists operating lightweight hybrid remotely operated vehicles (HROV) in exploratory missions of mapping and surveying. The new capabilities, under development within the Spanish National project OMNIUS, provide a new layer of autonomy for HROVs by exploring three key concepts: Omni-directional optical sensing for collaborative immersive exploration, Proximity safety awareness and Online mapping during mission time.Peer Reviewe

Efficient topology estimation for large scale optical mapping

Large scale image mosaicing methods are in great demand among scientists who study different aspects of the seabed, and have been fostered by impressive advances in the capabilities of underwater robots in gathering optical data from the seafloor. Cost and weight constraints mean that lowcost Remotely operated vehicles (ROVs) usually have a very limited number of sensors. When a low-cost robot carries out a seafloor survey using a down-looking camera, it usually follows a predetermined trajectory that provides several non time-consecutive overlapping image pairs. Finding these pairs (a process known as topology estimation) is indispensable to obtaining globally consistent mosaics and accurate trajectory estimates, which are necessary for a global view of the surveyed area, especially when optical sensors are the only data source. This thesis presents a set of consistent methods aimed at creating large area image mosaics from optical data obtained during surveys with low-cost underwater vehicles. First, a global alignment method developed within a Feature-based image mosaicing (FIM) framework, where nonlinear minimisation is substituted by two linear steps, is discussed. Then, a simple four-point mosaic rectifying method is proposed to reduce distortions that might occur due to lens distortions, error accumulation and the difficulties of optical imaging in an underwater medium. The topology estimation problem is addressed by means of an augmented state and extended Kalman filter combined framework, aimed at minimising the total number of matching attempts and simultaneously obtaining the best possible trajectory. Potential image pairs are predicted by taking into account the uncertainty in the trajectory. The contribution of matching an image pair is investigated using information theory principles. Lastly, a different solution to the topology estimation problem is proposed in a bundle adjustment framework. Innovative aspects include the use of fast image similarity criterion combined with a Minimum spanning tree (MST) solution, to obtain a tentative topology. This topology is improved by attempting image matching with the pairs for which there is the most overlap evidence. Unlike previous approaches for large-area mosaicing, our framework is able to deal naturally with cases where time-consecutive images cannot be matched successfully, such as completely unordered sets. Finally, the efficiency of the proposed methods is discussed and a comparison made with other state-of-the-art approaches, using a series of challenging datasets in underwater scenariosEls mètodes de generació de mosaics de gran escala gaudeixen d’una gran demanda entre els científcs que estudien els diferents aspectes del fons submarí, afavorida pels impressionants avenços en les capacitats dels robots submarins per a l’obtenció de dades ptiques del fons. El cost i el pes constitueixen restriccions que impliquen que els vehicles operats remotament disposin habitualment d’un nombre limitat de sensors. Quan un robot de baix cost duu a terme una exploració del fons submarí utilitzant una càmera apuntant cap al terreny, aquest segueix habitualment una trajectòria que dóna com a resultat diverses parelles d’imatges amb superposició de manera sequencial. Trobar aquestes parelles (estimació de la topologia) és una tasca indispensable per a l’obtenció de mosaics globalment consistents així com una estimació de trajectòria precisa, necessària per disposar d’una visió global de la regió explorada, especialment en el cas en què els sensors òptics constitueixen la única font de dades. Aquesta tesi presenta un conjunt de mètodes robustos destinats a la creació de mosaics d’àrees de grans dimensions a partir de dades òptiques (imatges) obtingudes durant exploracions realitzades amb vehicles submarins de baix cost. En primer lloc, es presenta un mètode d’alineament global desenvolupat en el context de la generació de mosaics basat en característiques 2D, substituint una minimització no lineal per dues etapes lineals. Així mateix, es proposa un mètode simple de rectificació de mosaics basat en quatre punts per tal de reduir les distorsions que poden aparèixer a causa de la distorsió de les lents, l’acumulació d’errors i les dificultats d’adquisició d’imatges en el medi submarí. El problema de l’estimació de la topologia s’aborda mitjanant la combinació d’un estat augmentat amb un altre de Kalman estès, amb l’objectiu de minimitzar el nombre total d’intents de cerca de correspondències i obtenir simultàniament la millor trajectòria possible. La predicció de les parelles d’imatges potencials té en compte la incertesa de la trajectòria, i la contribució de l’obtenció de correspondències per a un parell d’imatges s’estudia d’acord amb principis de la teoria de la informació. Així mateix, el problema de l’estimació de la topologia és abordat en el context d’un alineament global. Les innovacions inclouen l’ús d’un criteri ràpid per a determinació de la similitud entre imatges combinat amb una solució basada en arbres d’expansió mínima, per tal d’obtenir una topologia provisional. Aquesta topologia és millorada mitjançant l’intent de cerca de correspondències entre parelles d’imatges amb major probabilitat de superposició. Contràriament al que succeïa en solucions prèvies per a la construcció de mosaics de grans àrees, el nostre entorn de treball és capaç de tractar amb casos en què imatges consecutives en el temps no han pogut ser relacionades satisfactòriament, com és el cas de conjunts d’imatges totalment desordenats. Finalment, es discuteix l’eficiència del mètode proposat i es compara amb altres solucions de l’estat de l’art, utilitzant una sèrie de conjunts de dades complexos en escenaris subaquàtics

A Two-Step Global Alignment Method for Feature-Based Image Mosaicing

Image mosaicing sits at the core of many optical mapping applications with mobile robotic platforms. As these platforms have been evolving rapidly and increasing their capabilities, the amount of data they are able to collect is increasing drastically. For this reason, the necessity for efficient methods to handle and process such big data has been rising from different scientific fields, where the optical data provides valuable information. One of the challenging steps of image mosaicing is finding the best image-to-map (or mosaic) motion (represented as a planar transformation) for each image while considering the constraints imposed by inter-image motions. This problem is referred to as Global Alignment (GA) or Global Registration, which usually requires a non-linear minimization. In this paper, following the aforementioned motivations, we propose a two-step global alignment method to obtain globally coherent mosaics with less computational cost and time. It firstly tries to estimate the scale and rotation parameters and then the translation parameters. Although it requires a non-linear minimization, Jacobians are simple to compute and do not contain the positions of correspondences. This allows for saving computational cost and time. It can be also used as a fast way to obtain an initial estimate for further usage in the Symmetric Transfer Error Minimization (STEMin) approach. We presented experimental and comparative results on different datasets obtained by robotic platforms for mapping purposes

Rule Evaluation Algorithm for Semantic Query Optimisation

Semantic Query Optimisation (SQO) in Relational Database Management Systems (RDMSs) is a query optimisation approach which uses rules learned from past queries in order to execute new queries more intelligently without accessing database, whenever possible. The approach is composed of several components: Query Representation, Query Optimisation, Automatic Rule Derivation and Rule Maintenance. This paper focused on the query optimisation component. In RDMSs, during the traditional SQO, different alternative queries of a given query can be constructed using matching rule(s) from the rule set, and then its optimiser selects one of the alternatives as an optimum query which will give the same answer set but it can be executed faster than the original query. One of the main problems occurs during this process is to have many matched rules e.g., if the number of the rules is N, the number of the alternative queries is 2N − 1. The construction and the optimisation of these alternatives also take time in addition to the execution of the query. In order to overcome this problem, in this paper we propose a new Rule Evaluation Algorithm. The main goal of the algorithm is to evaluate matching rule(s) and select useful/promising rules. And then use selected rules to construct an optimum query. The algorithm can answer the question of the utility of rules in the query optimisation. The system of the approach based on the algorithm has been implemented and its computational results are given. The experimental results show that the algorithm can trim the number of the rules significantly

Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

mage registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments

Rule Evaluation Algorithm for Semantic Query

Abstract: Semantic Query Optimisation (SQO) in Relational Database Management Systems (RDMSs) is a query optimisation approach which uses rules learned from past queries in order to execute new queries more intelligently without accessing database, whenever possible. The approach is composed of several components: Query Representation, Query Optimisation, Automatic Rule Derivation and Rule Maintenance. This paper focused on the query optimisation component. In RDMSs, during the traditional SQO, different alternative queries of a given query can be constructed using matching rule(s) from the rule set, and then its optimiser selects one of the alternatives as an optimum query which will give the same answer set but it can be executed faster than the original query. One of the main problems occurs during this process is to have many matched rules e.g., if the number of the rules isN, the number of the alternative queries is2 N −1. The construction and the optimisation of these alternatives also take time in addition to the execution of the query. In order to overcome this problem, in this paper we propose a new Rule Evaluation Algorithm. The main goal of the algorithm is to evaluate matching rule(s) and select useful/promising rules. And then use selected rules to construct an optimum query. The algorithm can answer the question of the utility of rules in the query optimisation. The system of the approach based on the algorithm has been implemented and its computational results are given. The experimental results show that the algorithm can trim the number of the rules significantly

Topology Graph Pruning for Optical Mapping Methods using Edge Betweenness Centrality

Optical mapping is one of the most widely used application areas of low-cost robotic platforms. These platforms are in favor as they are relatively easy to use, to operate and to maintain. Acquired optical data (in the form of video and/or image) are valuable sources of information for both online (e.g., navigation, localization, mapping, and others) and offline processes (scientific interpretations, change detection, mapping, and others). The amount of data acquired has been continuously growing thanks to the emerging capabilities of mobile platforms in terms of autonomy allowing longer surveying time. This increases the need for fast and efficient methods to process the obtained data. Creating optical 2D maps from acquired data is composed of mainly image matching, trajectory estimation (Global Alignment (GA)) and image blending steps. In this paper, we discuss the usage of Edge Betweenness Centrality (EBC) concept to reduce the total number of overlapping image pairs to be used in the GA step. EBC allows selecting the image pairs that play a relatively key role in the topology graph. We also discuss the usage of graph energy as a decision criterion during image mosaicing iterations. We present experiments with several datasets to show the performance of the proposed method

Algorithmically Improved Framework for Image-only Robotic Mapping

Over the past two decades, technological developments in the robotics field have made it possible to gather data from areas that are hostile and not-reachable environments for humans. In line with these advancements in data collecting tools and procedures, the need and demand for computationally efficient methods for processing the gathered data have been increased from different science and engineering fields. Among the others, optical data is one of the main data sources that low-cost robotic vehicles can obtain easily nowadays. Due to the different limitations, obtained optical data usually cannot cover a large area in a single image. Therefore, optical mapping methods (image mosaicing) are needed to create higher resolution maps by combining comparatively smaller resolution images. These methods rely on pairwise image registration and one of the main bottlenecks in the case of image-only information available is that the quadratic growth of image matching attempts with respect to the total number of images. In this paper, we propose an algorithmically improved end-to-end framework for creating 2D optical maps from a set of randomly ordered images with the aim of reducing computational efforts needed via lowering the total number of image matching attempts. We present extensive and comparative experimental results with its counterpart approach using four real datasets obtained from the underwater environment using Unmanned Underwater Vehicles (UUVs)