Multi-resolution graph-based representation for analysis of large histo-pathological images

La exploración de muestras histopatológicas es potencialmente una importante fuente de información para el diagnóstico de enfermedades, educación y entrenamiento médico. Convencionalmente, este tipo de muestras se observan a través de un microscopio óptico, sin embargo, recientes avances tecnológicos en el área de imagino logia han favorecido el desarrollo de la microscopía virtual, un conjunto de herramientas que permiten la navegación de versiones digitales de placas histopatológicas, también llamadas Placas Virtuales (PV). Ya que las PV son un conjunto de imágenes de campos visuales microscópicos, estas son imágenes de alta resolución. El gran tamaño de las PV representa un cuello de botella para cualquier estrategia de navegación, introduciendo considerables retardos en la presentación de la información al usuario y produciendo en consecuencia, navegaciones poco fluidas. Por esto, el desarrollo de métodos para acelerar la interacción en este tipo de aplicaciones impulsara su uso en áreas como la tele patología. Una de las aproximaciones más aceptadas respecto a este problema son las estrategias de predicción, en las cuales solo las regiones de interés son enviadas al usuario. Por tanto el reto en este tipo de aproximaciones es identificar las regiones relevantes para el patólogo, es decir aquellas que contienen significado semántico para el diagnostico. Los modelos de atención visual intentan emular la percepción visual humana, permitiendo identificar las pocas regiones de una escena que contienen la mayor cantidad de información. Sin embargo, la complejidad de las imágenes histopatológicas hace que este tipo de modelos sea insuficiente para identificar estructuras relevantes dentro de la muestra y se hace necesario utilizar nuevas fuentes de información. En este trabajo se presenta una novedosa aproximación estadística que permite construir un mapa de información relevante de la PV con el fin de permitir navegaciones fluidas en un microscopio virtual. Este trabajo utiliza como una primera aproximación a las regiones relevantes un mapa de saliencia obtenido a partir de un modelo de atención visual \bottom-up", el cual es modificado por conocimiento \top-down. Obtenido a partir de navegaciones de patólogos expertos sobre PVs. Las regiones de la PV son estructuradas en un grafo tipo árbol, de acuerdo a su nivel de relevancia, en esta estructura los nodos representan regiones espaciales de la PV y los arcos representan relaciones de inclusión entre ellas. El método presentado alcanza en promedio, una capacidad de predicción del 67 % de regiones de interés que fueron visitadas en una nueva navegación, asumiendo un caché de solo el 20 % del tamaño total de la placa virtual. Adicionalmente, con respecto a un método más simple, i. e. sin la representación del grafo, el método propuesto mejora en un 3 % la capacidad de identificación de regiones relevantes, lo cual es significativo en este contexto, ya que un porcentaje como este puede representar regiones de mega pixeles en una PV.Exploration of complete histological samples is a very important source of information for diagnosis, learning and medical training. These samples have been conventionally observed through an optical microscope, but recent advances in imaging technology have enabled the development of virtual microscopy, a set of tools that allows navigation of digitized versions of tissue samples (virtual slides or VS). Since VS are the set of several microscopic visual �elds they are high resolution images with huge storage spaces. The size of these images repre- sents a bottle-neck for any navigation strategy, introducing considerable delays in displaying information to the user, which results in non- uid navigations. Development of methodo- logies for accelerate interaction with such volume of data, represents a reinforcement of its use in real applications as telepathology. One of the most accepted methodologies regarding this problem are based on prediction policies, in which only the regions of current interest are provided to the user. Then, the challenge in these approaches is to identify relevant regions, which for VS exploration enclose semantic meaning for diagnosis. Classical visual attention models emulates the human visual perception allowing to identify relevant regions with highest quantity of information. However, complexity of histo-pathological images make insu�cient these models to identify all relevant structures in the sample, furthermore new information sources that lies the identi�cation of salient features is required. In this work is presented a novel statistical hybrid approach which enable to build up relevant informa- tion VS maps to achieve uent virtual navigation. This approach, uses as �rst relevance approximation, a saliency map obtained from a bottom-up visual attention model, which is modi�ed by a top-down relevance information obtained from actual VS explorations of expert pathologists. Image regions are organized in a graph structure according it's levels of relevance, in this structure nodes are related to spatial regions and edges represent belonging relationships between them. The method herein introduced achieve in average, a prediction capacity of 67 % of RoIs that were visited in a new navigation, assuming a cache of only 20 % of total size of VS. Additionally, with respect to a simpler method, i.e. without graph representation, graph-based method outperforms RoIs identi�cation capacity in about 3 % which is signi�cant in this context since such a percentage could represent regions of mega- pixels of a VS.Maestrí

Coarse-to-fine textures retrieval in the JPEG 2000 compressed domain for fast browsing of large image databases

In many applications, the amount and resolution of digi- tal images have significantly increased over the past few years. For this reason, there is a growing interest for techniques allowing to efficiently browse and seek information inside such huge data spaces. JPEG 2000, the latest compression standard from the JPEG committee, has several interesting features to handle very large images. In this paper, these fea- tures are used in a coarse-to-fine approach to retrieve specific information in a JPEG 2000 code-stream while minimizing the computational load required by such processing. Practically, a cascade of classifiers exploits the bit-depth and resolution scalability features intrinsically present in JPEG 2000 to progressively refine the classification process. Comparison with existing techniques is made in a texture-retrieval task and shows the efficiency of such approach

Scalable Remote Rendering using Synthesized Image Quality Assessment

Depth-image-based rendering (DIBR) is widely used to support 3D interactive graphics on low-end mobile devices. Although it reduces the rendering cost on a mobile device, it essentially turns such a cost into depth image transmission cost or bandwidth consumption, inducing performance bottleneck to a remote rendering system. To address this problem, we design a scalable remote rendering framework based on synthesized image quality assessment. Specially, we design an efficient synthesized image quality metric based on Just Noticeable Distortion (JND), properly measuring human perceived geometric distortions in synthesized images. Based on this, we predict quality-aware reference viewpoints, with viewpoint intervals optimized by the JND-based metric. An adaptive transmission scheme is also developed to control depth image transmission based on perceived quality and network bandwidth availability. Experiment results show that our approach effectively reduces transmission frequency and network bandwidth consumption with perceived quality on mobile devices maintained. A prototype system is implemented to demonstrate the scalability of our proposed framework to multiple clients

Métodos computacionais para otimização de desempenho em redes de imagem médica

Over the last few years, the medical imaging has consolidated its position as a major mean of clinical diagnosis. The amount of data generated by the medical imaging practice is increasing tremendously. As a result, repositories are turning into rich databanks of semi-structured data related to patients, ailments, equipment and other stakeholders involved in the medical imaging panorama. The exploration of these repositories for secondary uses of data promises to elevate the quality standards and efficiency of the medical practice. However, supporting these advanced usage scenarios in traditional institutional systems raises many technical challenges that are yet to be overcome. Moreover, the reported poor performance of standard protocols opened doors to the general usage of proprietary solutions, compromising the interoperability necessary for supporting these advanced scenarios. This thesis has researched, developed, and now proposes a series of computer methods and architectures intended to maximize the performance of multi-institutional medical imaging environments. The methods are intended to improve the performance of standard protocols for medical imaging content discovery and retrieval. The main goal is to use them to increase the acceptance of vendor-neutral solutions through the improvement of their performance. Moreover, it intends to promote the adoption of such standard technologies in advanced scenarios that are still a mirage nowadays, such as clinical research or data analytics directly on top of live institutional repositories. Finally, these achievements will facilitate the cooperation between healthcare institutions and researchers, resulting in an increment of healthcare quality and institutional efficiency.As diversas modalidades de imagem médica têm vindo a consolidar a sua posição dominante como meio complementar de diagnóstico. O número de procedimentos realizados e o volume de dados gerados aumentou significativamente nos últimos anos, colocando pressão nas redes e sistemas que permitem o arquivo e distribuição destes estudos. Os repositórios de estudos imagiológicos são fontes de dados ricas contendo dados semiestruturados relacionados com pacientes, patologias, procedimentos e equipamentos. A exploração destes repositórios para fins de investigação e inteligência empresarial, tem potencial para melhorar os padrões de qualidade e eficiência da prática clínica. No entanto, estes cenários avançados são difíceis de acomodar na realidade atual dos sistemas e redes institucionais. O pobre desempenho de alguns protocolos standard usados em ambiente de produção, conduziu ao uso de soluções proprietárias nestes nichos aplicacionais, limitando a interoperabilidade de sistemas e a integração de fontes de dados. Este doutoramento investigou, desenvolveu e propõe um conjunto de métodos computacionais cujo objetivo é maximizar o desempenho das atuais redes de imagem médica em serviços de pesquisa e recuperação de conteúdos, promovendo a sua utilização em ambientes de elevados requisitos aplicacionais. As propostas foram instanciadas sobre uma plataforma de código aberto e espera-se que ajudem a promover o seu uso generalizado como solução vendor-neutral. As metodologias foram ainda instanciadas e validadas em cenários de uso avançado. Finalmente, é expectável que o trabalho desenvolvido possa facilitar a investigação em ambiente hospitalar de produção, promovendo, desta forma, um aumento da qualidade e eficiência dos serviços.Programa Doutoral em Engenharia Informátic

Efficient interaction with large medical imaging databases

Everyday, a wide quantity of hospitals and medical centers around the world are producing large amounts of imaging content to support clinical decisions, medical research, and education. With the current trend towards Evidence-based medicine, there is an increasing need of strategies that allow pathologists to properly interact with the valuable information such imaging repositories host and extract relevant content for supporting decision making. Unfortunately, current systems are very limited at providing access to content and extracting information from it because of different semantic and computational challenges. This thesis presents a whole pipeline, comprising 3 building blocks, that aims to to improve the way pathologists and systems interact. The first building block consists in an adaptable strategy oriented to ease the access and visualization of histopathology imaging content. The second block explores the extraction of relevant information from such imaging content by exploiting low- and mid-level information obtained from from morphology and architecture of cell nuclei. The third block aims to integrate high-level information from the expert in the process of identifying relevant information in the imaging content. This final block not only attempts to deal with the semantic gap but also to present an alternative to manual annotation, a time consuming and prone-to-error task. Different experiments were carried out and demonstrated that the introduced pipeline not only allows pathologist to navigate and visualize images but also to extract diagnostic and prognostic information that potentially could support clinical decisions.Resumen: Diariamente, gran cantidad de hospitales y centros médicos de todo el mundo producen grandes cantidades de imágenes diagnósticas para respaldar decisiones clínicas y apoyar labores de investigación y educación. Con la tendencia actual hacia la medicina basada en evidencia, existe una creciente necesidad de estrategias que permitan a los médicos patólogos interactuar adecuadamente con la información que albergan dichos repositorios de imágenes y extraer contenido relevante que pueda ser empleado para respaldar la toma de decisiones. Desafortunadamente, los sistemas actuales son muy limitados en cuanto al acceso y extracción de contenido de las imágenes debido a diferentes desafíos semánticos y computacionales. Esta tesis presenta un marco de trabajo completo para patología, el cual se compone de 3 bloques y tiene como objetivo mejorar la forma en que interactúan los patólogos y los sistemas. El primer bloque de construcción consiste en una estrategia adaptable orientada a facilitar el acceso y la visualización del contenido de imágenes histopatológicas. El segundo bloque explora la extracción de información relevante de las imágenes mediante la explotación de información de características visuales y estructurales de la morfología y la arquitectura de los núcleos celulares. El tercer bloque apunta a integrar información de alto nivel del experto en el proceso de identificación de información relevante en las imágenes. Este bloque final no solo intenta lidiar con la brecha semántica, sino que también presenta una alternativa a la anotación manual, una tarea que demanda mucho tiempo y es propensa a errores. Se llevaron a cabo diferentes experimentos que demostraron que el marco de trabajo presentado no solo permite que el patólogo navegue y visualice imágenes, sino que también extraiga información de diagnóstico y pronóstico que potencialmente podría respaldar decisiones clínicas.Doctorad

Modeling and acceleration of content delivery in world wide web

Ph.DDOCTOR OF PHILOSOPH

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage