Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system

Identificação de Pedestres por meio de mapas de densidade construídos com ASIFT e Fluxo Óptico

Não consta

Identificação de Pedestres por meio de mapas de densidade construídos com ASIFT e Fluxo Óptico

Não consta

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Deep Learning Techniques for Medical Image Classification

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsIn recent years, artificial intelligence (AI) has been applied in many fields to address complex and critical real-world tasks. Deep learning rises as a subfield of AI, where artificial neural networks (ANN) are used to map complicated functions, which can be challenging even for experienced users. One of the ANN variants is called convolutional neural network (CNN), which has shown great potential in image processing by providing state-of-the-art results for many significant image processing challenges. The medical field can significantly benefit from AI usage, especially in the medical image classification domain. In this doctoral dissertation, we applied different AI techniques to analyze medical images and to give the physicians a second opinion or reduce the time and effort needed for the image classification. Initially, we reviewed several studies that were published to discuss the transfer learning of CNNs. Afterward, we studied different hyperparameters that need to be optimized for CNNs to be trained accurately. Lastly, we proposed a novel CNN architecture to help in the classification of histopathology images

Visual image processing in various representation spaces for documentary preservation

This thesis establishes an advanced image processing framework for the enhancement and restoration of historical document images (HDI) in both intensity (gray-scale or color) and multispectral (MS) representation spaces. It provides three major contributions: 1) the binarization of gray-scale HDI; 2) the visual quality restoration of MS HDI; and 3) automatic reference data (RD) estimation for HDI binarization. HDI binarization is one of the enhancement techniques that produces bi-level information which is easy to handle using methods of analysis (OCR, for instance) and is less computationally costly to process than 256 levels of grey or color images. Restoring the visual quality of HDI in an MS representation space enhances their legibility, which is not possible with conventional intensity-based restoration methods, and HDI legibility is the main concern of historians and librarians wishing to transfer knowledge and revive ancient cultural heritage. The use of MS imaging systems is a new and attractive research trend in the field of numerical processing of cultural heritage documents. In this thesis, these systems are also used for automatically estimating more accurate RD to be used for the evaluation of HDI binarization algorithms in order to track the level of human performance. Our first contribution, which is a new adaptive method of intensity-based binarization, is defined at the outset. Since degradation is present over document images, binarization methods must be adapted to handle degradation phenomena locally. Unfortunately, these methods are not effective, as they are not able to capture weak text strokes, which results in a deterioration of the performance of character recognition engines. The proposed approach first detects a subset of the most probable text pixels, which are used to locally estimate the parameters of the two classes of pixels (text and background), and then performs a simple maximum likelihood (ML) to locally classify the remaining pixels based on their class membership. To the best of our knowledge, this is the first time local parameter estimation and classification in an ML framework has been introduced for HDI binarization with promising results. A limitation of this method in the case with as the intensity-based methods of enhancement is that they are not effective in dealing with severely degraded HDI. Developing more advanced methods based on MS information would be a promising alternative avenue of research. In the second contribution, a novel approach to the visual restoration of HDI is defined. The approach is aimed at providing end users (historians, librarians, etc..) with better HDI visualization, specifically; it aims to restore them from degradations, while keeping the original appearance of the HDI intact. Practically, this problem cannot be solved by conventional intensity-based restoration methods. To cope with these limitations, MS imaging is used to produce additional spectral images in the invisible light (infrared and ultraviolet) range, which gives greater contrast to objects in the documents. The inpainting-based variational framework proposed here for HDI restoration involves isolating the degradation phenomena in the infrared spectral images, and then inpainting them in the visible spectral images. The final color image to visualize is therefore reconstructed from the restored visible spectral images. To the best of our knowledge, this is the first time the inpainting technique has been introduced for MS HDI. The experimental results are promising, and our objective, in collaboration with the BAnQ (Bibliothèque et Archives nationales de Québec), is to push heritage documents into the public domain and build an intelligent engine for accessing them. It is useful to note that the proposed model can be extended to other MS-based image processing tasks. Our third contribution is presented, which is to consider a new problem of RD (reference data) estimation, in order to show the importance of working with MS images rather than gray-scale or color images. RDs are mandatory for comparing different binarization algorithms, and they are usually generated by an expert. However, an expert’s RD is always subject to mislabeling and judgment errors, especially in the case of degraded data in restricted representation spaces (gray-scale or color images). In the proposed method, multiple RD generated by several experts are used in combination with MS HDI to estimate new, more accurate RD. The idea is to include the agreement of experts about labels and the multivariate data fidelity in a single Bayesian classification framework to estimate the a posteriori probability of new labels forming the final estimated RD. Our experiments show that estimated RD are more accurate than an expert’s RD. To the best of our knowledge, no similar work to combine binary data and multivariate data for the estimation of RD has been conducted

Design, Development and Implementation Framework for a Postgraduate Non-Surgical Aesthetics Curriculum

Non-surgical aesthetics (NSA) procedures are primarily performed in private clinics away from traditional teaching hospital settings, establishing structured training and education in these procedures during residency training has been challenging. The objective of this study was to design and develop an evidence-based postgraduate curriculum in non-surgical aesthetics. It necessitated determining the current state of training and education for NSA procedures in postgraduate clinical education. Following a design-based research approach, a subsequent systematic literature review and a cross-sectional global-needs assessment study established the need for such a curriculum. Subsequent literature reviews and series of global Delphi studies have informed and guided the design and development of the conceptual framework, core curriculum content and finally, the implementation framework to facilitate the smooth delivery of the programme. The research also incorporated pilot studies for teaching methodology, assessment strategies like “objective structured practical examination (OSPE) and objective structured clinical examination (OSCE)”, which has shown to be very effective. The conceptual framework for curriculum design and development in NSA emerged from the global Delphi study. The conceptual framework is anchored on critical thinking and uses enquiry-based learning to develop information mastery, skills, and values and attitude. Moreover, relevant threshold concepts guided the construction of learning outcomes mapped against the core curriculum. The finding of this study is a crucial first step in bringing an evidence-based structure to training and education in NSA. This thesis will act as a ‘blueprint’ for the policymakers and program directors while curating a postgraduate programme in NSA

Généralisation du diagramme de Voronoï et placement de formes géométriques complexes dans un nuage de points.

La géométrie algorithmique est une discipline en pleine expansion dont l'objet est la conception d'algorithmes résolvant des problèmes géométriques. De tels algorithmes sont très utiles notamment dans l'ingénierie, l'industrie et le multimédia. Pour être performant, il est fréquent qu'un algorithme géométrique utilise des structures de données spécialisées.Nous nous sommes intéressés à une telle structure : le diagramme de Voronoï et avons proposé une généralisation de celui-ci. Ladite généralisation résulte d'une extension du prédicat du disque vide (prédicat propre à toute région de Voronoï) à une union de disques. Nous avons analysé les régions basées sur le prédicat étendu et avons proposé des méthodes pour les calculer par ordinateur.Par ailleurs, nous nous sommes intéressés aux problèmes de placement de formes , thème récurrent en géométrie algorithmique. Nous avons introduit un formalisme universel pour de tels problèmes et avons, pour la première fois, proposé une méthode de résolution générique, en ce sens qu'elle est apte à résoudre divers problèmes de placement suivant un même algorithme.Nos travaux présentent, d'une part, l'avantage d'élargir le champ d'application de structures de données basées sur Voronoï. D'autre part, ils facilitent de manière générale l'utilisation de la géométrie algorithmique, en unifiant définitions et algorithmes associés aux problèmes de placement de formes.Computational geometry is an active branch of computer science whose goal is the design of efficient algorithms solving geometric problems. Such algorithms are useful in domains like engineering, industry and multimedia. In order to be efficient, algorithms often use special data structures.In this thesis we focused on such a structure: the Voronoi diagram. We proposed a new generalized diagram. We have proceeded by extending the empty disk predicate (satisfied by every Voronoi region) to an arbitrary union of disks. We have analyzed the new plane regions based on the extended predicate, and we designed algorithms for computing them.Then, we have considered another topic, which is related to the first one: shape placement problems. Such problems have been studied repeatedly by researchers in computational geometry. We introduced new notations along with a global framework for such problems. We proposed, for the first time a generic method, which is able to solve various placement problems using a single algorithm.Thus, our work extend the scope of Voronoi based data structures. It also simplifies the practical usage of placement techniques by unifying the associated definitions and algorithms.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Généralisation du diagramme de Voronoï et placement de formes géométriques complexes dans un nuage de points.

La géométrie algorithmique est une discipline en pleine expansion dont l'objet est la conception d'algorithmes résolvant des problèmes géométriques. De tels algorithmes sont très utiles notamment dans l'ingénierie, l'industrie et le multimédia. Pour être performant, il est fréquent qu'un algorithme géométrique utilise des structures de données spécialisées.Nous nous sommes intéressés à une telle structure : le diagramme de Voronoï et avons proposé une généralisation de celui-ci. Ladite généralisation résulte d'une extension du prédicat du disque vide (prédicat propre à toute région de Voronoï) à une union de disques. Nous avons analysé les régions basées sur le prédicat étendu et avons proposé des méthodes pour les calculer par ordinateur.Par ailleurs, nous nous sommes intéressés aux problèmes de placement de formes , thème récurrent en géométrie algorithmique. Nous avons introduit un formalisme universel pour de tels problèmes et avons, pour la première fois, proposé une méthode de résolution générique, en ce sens qu'elle est apte à résoudre divers problèmes de placement suivant un même algorithme.Nos travaux présentent, d'une part, l'avantage d'élargir le champ d'application de structures de données basées sur Voronoï. D'autre part, ils facilitent de manière générale l'utilisation de la géométrie algorithmique, en unifiant définitions et algorithmes associés aux problèmes de placement de formes.Computational geometry is an active branch of computer science whose goal is the design of efficient algorithms solving geometric problems. Such algorithms are useful in domains like engineering, industry and multimedia. In order to be efficient, algorithms often use special data structures.In this thesis we focused on such a structure: the Voronoi diagram. We proposed a new generalized diagram. We have proceeded by extending the empty disk predicate (satisfied by every Voronoi region) to an arbitrary union of disks. We have analyzed the new plane regions based on the extended predicate, and we designed algorithms for computing them.Then, we have considered another topic, which is related to the first one: shape placement problems. Such problems have been studied repeatedly by researchers in computational geometry. We introduced new notations along with a global framework for such problems. We proposed, for the first time a generic method, which is able to solve various placement problems using a single algorithm.Thus, our work extend the scope of Voronoi based data structures. It also simplifies the practical usage of placement techniques by unifying the associated definitions and algorithms.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Arquitectura de percepción bioinspirada basada en atención para un robot social

La atención desempeña un papel fundamental, tanto para los seres humanos como para los sistemas artificiales, ya que es una habilidad crucial que nos permite interactuar de manera efectiva con nuestro entorno. Desde la infancia hasta la edad adulta, la atención nos ayuda a concentrarnos en estímulos relevantes, procesar información de manera eficiente y responder a estímulos emocionales y sociales. Además, de influir en aspectos importantes de nuestras vidas, como el aprendizaje y las interacciones sociales. La implementación de mecanismos de atención en sistemas artificiales tiene como objetivo aprovechar los beneficios de esta habilidad fundamental. Esto se traduce en una mejora en el procesamiento de información, la toma de decisiones y la interacción con el entorno. La atención en sistemas artificiales es un área de investigación en constante desarrollo, con el propósito de mejorar la capacidad de los sistemas inteligentes en diversas aplicaciones. Uno de los campos donde más se ha estudiado el concepto de la atención es en visión artificial, en la cual se utiliza para resaltar regiones relevantes en las imágenes, lo que mejora el análisis y el reconocimiento de objetos, mientras que en la robótica, la atención permite a los robots enfocarse en objetos o eventos específicos, mejorando su capacidad de reacción y ejecución de tareas. Por este motivo, en este trabajo se propone un sistema de percepción bioinspirado basado en atención diseñado para mejorar la interacción humano-robot. Este sistema está diseñado para localizar el foco de atención del robot en cada momento teniendo en cuenta la tarea actual, los estímulos disponibles y el estado interno del robot. El sistema integra fenómenos bioinspirados como la inhibición al retorno, la relocalización del foco de atención dependiendo de los estímulos, los conceptos de atención sostenida y puntual para el cambio en el foco de atención y de agregación de estímulos de forma exógena y endógena de forma independiente. Además, se ha integrado en una plataforma robótica y se ha validado su funcionamiento en diferentes aplicaciones. Este trabajo se ha abordado desde dos perspectivas: la ampliación de las capacidades perceptuales del robot y la mejora de la interacción gracias a la integración de la atención en la arquitectura software de las plataformas robóticas. Para ello, en este trabajo se han investigado los estímulos más relevantes para la atención en humanos y su integración en el ámbito de la robótica y como realizar la agregación y fusión multisensorial de estos desde un punto de vista basado en la atención, consiguiendo una representación del entorno y seleccionando la posición del foco de atención en cada momento. Por otro lado, se ha investigado la relevancia de la integración de este sistema artificial a una plataforma robótica en lo que respecta a la interacción humano-robot, lo que ha dado lugar a un estudio que explora esta idea.Attention plays a fundamental role for both humans and artificial systems, as it is a crucial skill that enables us to interact effectively with our environment. From childhood to adulthood, attention helps us to focus on relevant stimuli, process information efficiently, and respond to emotional and social stimuli. It also influences important aspects of our lives, such as learning and social interactions. The implementation of attention mechanisms in artificial systems aims to take advantage of the benefits of this fundamental ability. This translates into improved information processing, decision making and interaction with the environment. Attention in artificial systems is an area of research in constant development, with the purpose of improving the capacity of intelligent systems in various applications. The fields where the concept of attention has been most studied are computer vision and robotics. In computer vision, attention is used to highlight relevant areas in images, which improves object analysis and recognition, while in robotics, attention allows robots to focus on specific objects or events, improving their ability to react and perform tasks. For this reason, this work proposes a bio-inspired attention-based perception system designed to improve human-robot interaction. This system is designed to locate the focus of attention of the robot at each moment, taking into account the current task, the available stimuli and the internal state of the robot.Moreover, the architecture integrates bioinspired concepts such as return inhibition, stimulus-dependent relocation of the focus of attention, the concepts of sustained and punctual attention for the shift in the focus of attention and the aggregation of exogenous and endogenous stimuli independently are integrated. In addition to this, it has been integrated into a robotic platform, and its performance has been validated in different applications. This work has been approached from two perspectives: the increase of the perceptual capabilities of the robot and the improvement of the interaction thanks to the integration of attention in the software architecture of robotic platforms. To this end, in this work, we have investigated the most relevant stimuli for attention in humans and their integration in the robotics environment, and how to perform the aggregation and multisensory fusion of these from an attention-based point of view, achieving a representation of the environment and selecting the position of the focus of attention at each moment. On the other hand, we have investigated the relevance of the integration of this artificial system to a robotic platform in terms of human-robot interaction, leading to a study that explores this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Caballero.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Plinio Moreno Lópe