Sviluppo di un metodo di radioterapia adattativa tramite immagini portali e Cone-Beam CT

Il presente lavoro, svolto presso il servizio di Fisica Sanitaria dell’Azienda Ospedaliera Universitaria di Parma, consiste nello sviluppo di un metodo innovativo di radioterapia adattativa. Il metodo è stato applicato a pazienti affetti da varie patologie, trattati con tecnica VMAT, (Volumetric Modulated Arc Therapy), altamente conformata al target. Il metodo sviluppato si compone di due fasi: nella prima fase vengono effettuate due analisi su immagini portali, di ricostruzione della dose all'isocentro e l'analisi gamma 2D. Se almeno una di queste fallisce, si interviene con la seconda fase, che vede l'acquisizione della CBCT del paziente e la taratura in densità elettronica della stessa. Si calcola dunque il piano su CBCT, previa operazione di contouring da parte del medico e, infine, si esegue l'analisi gamma 3D sulle matrici di dose calcolate sulla CT e sulla CBCT del paziente, quantificando gli indici gamma sulle strutture PTV, CTV e OAR di interesse clinico. In base ai risultati, se necessario, si può intervenire sul piano di trattamento. Le analisi gamma 2D e 3D sono state svolte avvalendosi di un software toolkit chiamato GADD-23 (Gamma Analysis on 2D and 3D Dose Distributions) implementato e sviluppato appositamente in ambiente Matlab per questo lavoro di tesi; in particolare, la realizzazione di GADD-23 è stata resa possibile grazie all'interazione con due software di tipo open-source, Elastix e CERR, specifici per l’elaborazione e la registrazione di immagini mediche. I risultati ottenuti mostrano come il metodo sviluppato sia in grado di mettere in luce cambiamenti anatomici che alcuni pazienti hanno subìto, di tipo sistematico, in cui è possibile prendere in considerazione una ripianificazione del trattamento per correggerli, o di tipo casuale, sui quali può essere utile condurre l'attenzione del medico radioterapista, sebbene non sia necessario un replanning.

Parametric and structural self-adaptation of embedded systems using evolvable hardware

Los sistemas empotrados han sido concebidos tradicionalmente como sistemas de procesamiento específicos que realizan una tarea fija durante toda su vida útil. Para cumplir con requisitos estrictos de coste, tamaño y peso, el equipo de diseño debe optimizar su funcionamiento para condiciones muy específicas. Sin embargo, la demanda de mayor versatilidad, un funcionamiento más inteligente y, en definitiva, una mayor capacidad de procesamiento comenzaron a chocar con estas limitaciones, agravado por la incertidumbre asociada a entornos de operación cada vez más dinámicos donde comenzaban a ser desplegados progresivamente. Esto trajo como resultado una necesidad creciente de que los sistemas pudieran responder por si solos a eventos inesperados en tiempo diseño tales como: cambios en las características de los datos de entrada y el entorno del sistema en general; cambios en la propia plataforma de cómputo, por ejemplo debido a fallos o defectos de fabricación; y cambios en las propias especificaciones funcionales causados por unos objetivos del sistema dinámicos y cambiantes. Como consecuencia, la complejidad del sistema aumenta, pero a cambio se habilita progresivamente una capacidad de adaptación autónoma sin intervención humana a lo largo de la vida útil, permitiendo que tomen sus propias decisiones en tiempo de ejecución. Éstos sistemas se conocen, en general, como sistemas auto-adaptativos y tienen, entre otras características, las de auto-configuración, auto-optimización y auto-reparación. Típicamente, la parte soft de un sistema es mayoritariamente la única utilizada para proporcionar algunas capacidades de adaptación a un sistema. Sin embargo, la proporción rendimiento/potencia en dispositivos software como microprocesadores en muchas ocasiones no es adecuada para sistemas empotrados. En este escenario, el aumento resultante en la complejidad de las aplicaciones está siendo abordado parcialmente mediante un aumento en la complejidad de los dispositivos en forma de multi/many-cores; pero desafortunadamente, esto hace que el consumo de potencia también aumente. Además, la mejora en metodologías de diseño no ha sido acorde como para poder utilizar toda la capacidad de cómputo disponible proporcionada por los núcleos. Por todo ello, no se están satisfaciendo adecuadamente las demandas de cómputo que imponen las nuevas aplicaciones. La solución tradicional para mejorar la proporción rendimiento/potencia ha sido el cambio a unas especificaciones hardware, principalmente usando ASICs. Sin embargo, los costes de un ASIC son altamente prohibitivos excepto en algunos casos de producción en masa y además la naturaleza estática de su estructura complica la solución a las necesidades de adaptación. Los avances en tecnologías de fabricación han hecho que la FPGA, una vez lenta y pequeña, usada como glue logic en sistemas mayores, haya crecido hasta convertirse en un dispositivo de cómputo reconfigurable de gran potencia, con una cantidad enorme de recursos lógicos computacionales y cores hardware empotrados de procesamiento de señal y de propósito general. Sus capacidades de reconfiguración han permitido combinar la flexibilidad propia del software con el rendimiento del procesamiento en hardware, lo que tiene la potencialidad de provocar un cambio de paradigma en arquitectura de computadores, pues el hardware no puede ya ser considerado más como estático. El motivo es que como en el caso de las FPGAs basadas en tecnología SRAM, la reconfiguración parcial dinámica (DPR, Dynamic Partial Reconfiguration) es posible. Esto significa que se puede modificar (reconfigurar) un subconjunto de los recursos computacionales en tiempo de ejecución mientras el resto permanecen activos. Además, este proceso de reconfiguración puede ser ejecutado internamente por el propio dispositivo. El avance tecnológico en dispositivos hardware reconfigurables se encuentra recogido bajo el campo conocido como Computación Reconfigurable (RC, Reconfigurable Computing). Uno de los campos de aplicación más exóticos y menos convencionales que ha posibilitado la computación reconfigurable es el conocido como Hardware Evolutivo (EHW, Evolvable Hardware), en el cual se encuentra enmarcada esta tesis. La idea principal del concepto consiste en convertir hardware que es adaptable a través de reconfiguración en una entidad evolutiva sujeta a las fuerzas de un proceso evolutivo inspirado en el de las especies biológicas naturales, que guía la dirección del cambio. Es una aplicación más del campo de la Computación Evolutiva (EC, Evolutionary Computation), que comprende una serie de algoritmos de optimización global conocidos como Algoritmos Evolutivos (EA, Evolutionary Algorithms), y que son considerados como algoritmos universales de resolución de problemas. En analogía al proceso biológico de la evolución, en el hardware evolutivo el sujeto de la evolución es una población de circuitos que intenta adaptarse a su entorno mediante una adecuación progresiva generación tras generación. Los individuos pasan a ser configuraciones de circuitos en forma de bitstreams caracterizados por descripciones de circuitos reconfigurables. Seleccionando aquellos que se comportan mejor, es decir, que tienen una mejor adecuación (o fitness) después de ser evaluados, y usándolos como padres de la siguiente generación, el algoritmo evolutivo crea una nueva población hija usando operadores genéticos como la mutación y la recombinación. Según se van sucediendo generaciones, se espera que la población en conjunto se aproxime a la solución óptima al problema de encontrar una configuración del circuito adecuada que satisfaga las especificaciones. El estado de la tecnología de reconfiguración después de que la familia de FPGAs XC6200 de Xilinx fuera retirada y reemplazada por las familias Virtex a finales de los 90, supuso un gran obstáculo para el avance en hardware evolutivo; formatos de bitstream cerrados (no conocidos públicamente); dependencia de herramientas del fabricante con soporte limitado de DPR; una velocidad de reconfiguración lenta; y el hecho de que modificaciones aleatorias del bitstream pudieran resultar peligrosas para la integridad del dispositivo, son algunas de estas razones. Sin embargo, una propuesta a principios de los años 2000 permitió mantener la investigación en el campo mientras la tecnología de DPR continuaba madurando, el Circuito Virtual Reconfigurable (VRC, Virtual Reconfigurable Circuit). En esencia, un VRC en una FPGA es una capa virtual que actúa como un circuito reconfigurable de aplicación específica sobre la estructura nativa de la FPGA que reduce la complejidad del proceso reconfiguración y aumenta su velocidad (comparada con la reconfiguración nativa). Es un array de nodos computacionales especificados usando descripciones HDL estándar que define recursos reconfigurables ad-hoc: multiplexores de rutado y un conjunto de elementos de procesamiento configurables, cada uno de los cuales tiene implementadas todas las funciones requeridas, que pueden seleccionarse a través de multiplexores tal y como ocurre en una ALU de un microprocesador. Un registro grande actúa como memoria de configuración, por lo que la reconfiguración del VRC es muy rápida ya que tan sólo implica la escritura de este registro, el cual controla las señales de selección del conjunto de multiplexores. Sin embargo, esta capa virtual provoca: un incremento de área debido a la implementación simultánea de cada función en cada nodo del array más los multiplexores y un aumento del retardo debido a los multiplexores, reduciendo la frecuencia de funcionamiento máxima. La naturaleza del hardware evolutivo, capaz de optimizar su propio comportamiento computacional, le convierten en un buen candidato para avanzar en la investigación sobre sistemas auto-adaptativos. Combinar un sustrato de cómputo auto-reconfigurable capaz de ser modificado dinámicamente en tiempo de ejecución con un algoritmo empotrado que proporcione una dirección de cambio, puede ayudar a satisfacer los requisitos de adaptación autónoma de sistemas empotrados basados en FPGA. La propuesta principal de esta tesis está por tanto dirigida a contribuir a la auto-adaptación del hardware de procesamiento de sistemas empotrados basados en FPGA mediante hardware evolutivo. Esto se ha abordado considerando que el comportamiento computacional de un sistema puede ser modificado cambiando cualquiera de sus dos partes constitutivas: una estructura hard subyacente y un conjunto de parámetros soft. De esta distinción, se derivan dos lineas de trabajo. Por un lado, auto-adaptación paramétrica, y por otro auto-adaptación estructural. El objetivo perseguido en el caso de la auto-adaptación paramétrica es la implementación de técnicas de optimización evolutiva complejas en sistemas empotrados con recursos limitados para la adaptación paramétrica online de circuitos de procesamiento de señal. La aplicación seleccionada como prueba de concepto es la optimización para tipos muy específicos de imágenes de los coeficientes de los filtros de transformadas wavelet discretas (DWT, DiscreteWavelet Transform), orientada a la compresión de imágenes. Por tanto, el objetivo requerido de la evolución es una compresión adaptativa y más eficiente comparada con los procedimientos estándar. El principal reto radica en reducir la necesidad de recursos de supercomputación para el proceso de optimización propuesto en trabajos previos, de modo que se adecúe para la ejecución en sistemas empotrados. En cuanto a la auto-adaptación estructural, el objetivo de la tesis es la implementación de circuitos auto-adaptativos en sistemas evolutivos basados en FPGA mediante un uso eficiente de sus capacidades de reconfiguración nativas. En este caso, la prueba de concepto es la evolución de tareas de procesamiento de imagen tales como el filtrado de tipos desconocidos y cambiantes de ruido y la detección de bordes en la imagen. En general, el objetivo es la evolución en tiempo de ejecución de tareas de procesamiento de imagen desconocidas en tiempo de diseño (dentro de un cierto grado de complejidad). En este caso, el objetivo de la propuesta es la incorporación de DPR en EHW para evolucionar la arquitectura de un array sistólico adaptable mediante reconfiguración cuya capacidad de evolución no había sido estudiada previamente. Para conseguir los dos objetivos mencionados, esta tesis propone originalmente una plataforma evolutiva que integra un motor de adaptación (AE, Adaptation Engine), un motor de reconfiguración (RE, Reconfiguration Engine) y un motor computacional (CE, Computing Engine) adaptable. El el caso de adaptación paramétrica, la plataforma propuesta está caracterizada por: • un CE caracterizado por un núcleo de procesamiento hardware de DWT adaptable mediante registros reconfigurables que contienen los coeficientes de los filtros wavelet • un algoritmo evolutivo como AE que busca filtros wavelet candidatos a través de un proceso de optimización paramétrica desarrollado específicamente para sistemas caracterizados por recursos de procesamiento limitados • un nuevo operador de mutación simplificado para el algoritmo evolutivo utilizado, que junto con un mecanismo de evaluación rápida de filtros wavelet candidatos derivado de la literatura actual, asegura la viabilidad de la búsqueda evolutiva asociada a la adaptación de wavelets. En el caso de adaptación estructural, la plataforma propuesta toma la forma de: • un CE basado en una plantilla de array sistólico reconfigurable de 2 dimensiones compuesto de nodos de procesamiento reconfigurables • un algoritmo evolutivo como AE que busca configuraciones candidatas del array usando un conjunto de funcionalidades de procesamiento para los nodos disponible en una biblioteca accesible en tiempo de ejecución • un RE hardware que explota la capacidad de reconfiguración nativa de las FPGAs haciendo un uso eficiente de los recursos reconfigurables del dispositivo para cambiar el comportamiento del CE en tiempo de ejecución • una biblioteca de elementos de procesamiento reconfigurables caracterizada por bitstreams parciales independientes de la posición, usados como el conjunto de configuraciones disponibles para los nodos de procesamiento del array Las contribuciones principales de esta tesis se pueden resumir en la siguiente lista: • Una plataforma evolutiva basada en FPGA para la auto-adaptación paramétrica y estructural de sistemas empotrados compuesta por un motor computacional (CE), un motor de adaptación (AE) evolutivo y un motor de reconfiguración (RE). Esta plataforma se ha desarrollado y particularizado para los casos de auto-adaptación paramétrica y estructural. • En cuanto a la auto-adaptación paramétrica, las contribuciones principales son: – Un motor computacional adaptable mediante registros que permite la adaptación paramétrica de los coeficientes de una implementación hardware adaptativa de un núcleo de DWT. – Un motor de adaptación basado en un algoritmo evolutivo desarrollado específicamente para optimización numérica, aplicada a los coeficientes de filtros wavelet en sistemas empotrados con recursos limitados. – Un núcleo IP de DWT auto-adaptativo en tiempo de ejecución para sistemas empotrados que permite la optimización online del rendimiento de la transformada para compresión de imágenes en entornos específicos de despliegue, caracterizados por tipos diferentes de señal de entrada. – Un modelo software y una implementación hardware de una herramienta para la construcción evolutiva automática de transformadas wavelet específicas. • Por último, en cuanto a la auto-adaptación estructural, las contribuciones principales son: – Un motor computacional adaptable mediante reconfiguración nativa de FPGAs caracterizado por una plantilla de array sistólico en dos dimensiones de nodos de procesamiento reconfigurables. Es posible mapear diferentes tareas de cómputo en el array usando una biblioteca de elementos sencillos de procesamiento reconfigurables. – Definición de una biblioteca de elementos de procesamiento apropiada para la síntesis autónoma en tiempo de ejecución de diferentes tareas de procesamiento de imagen. – Incorporación eficiente de la reconfiguración parcial dinámica (DPR) en sistemas de hardware evolutivo, superando los principales inconvenientes de propuestas previas como los circuitos reconfigurables virtuales (VRCs). En este trabajo también se comparan originalmente los detalles de implementación de ambas propuestas. – Una plataforma tolerante a fallos, auto-curativa, que permite la recuperación funcional online en entornos peligrosos. La plataforma ha sido caracterizada desde una perspectiva de tolerancia a fallos: se proponen modelos de fallo a nivel de CLB y de elemento de procesamiento, y usando el motor de reconfiguración, se hace un análisis sistemático de fallos para un fallo en cada elemento de procesamiento y para dos fallos acumulados. – Una plataforma con calidad de filtrado dinámica que permite la adaptación online a tipos de ruido diferentes y diferentes comportamientos computacionales teniendo en cuenta los recursos de procesamiento disponibles. Por un lado, se evolucionan filtros con comportamientos no destructivos, que permiten esquemas de filtrado en cascada escalables; y por otro, también se evolucionan filtros escalables teniendo en cuenta requisitos computacionales de filtrado cambiantes dinámicamente. Este documento está organizado en cuatro partes y nueve capítulos. La primera parte contiene el capítulo 1, una introducción y motivación sobre este trabajo de tesis. A continuación, el marco de referencia en el que se enmarca esta tesis se analiza en la segunda parte: el capítulo 2 contiene una introducción a los conceptos de auto-adaptación y computación autonómica (autonomic computing) como un campo de investigación más general que el muy específico de este trabajo; el capítulo 3 introduce la computación evolutiva como la técnica para dirigir la adaptación; el capítulo 4 analiza las plataformas de computación reconfigurables como la tecnología para albergar hardware auto-adaptativo; y finalmente, el capítulo 5 define, clasifica y hace un sondeo del campo del hardware evolutivo. Seguidamente, la tercera parte de este trabajo contiene la propuesta, desarrollo y resultados obtenidos: mientras que el capítulo 6 contiene una declaración de los objetivos de la tesis y la descripción de la propuesta en su conjunto, los capítulos 7 y 8 abordan la auto-adaptación paramétrica y estructural, respectivamente. Finalmente, el capítulo 9 de la parte 4 concluye el trabajo y describe caminos de investigación futuros. ABSTRACT Embedded systems have traditionally been conceived to be specific-purpose computers with one, fixed computational task for their whole lifetime. Stringent requirements in terms of cost, size and weight forced designers to highly optimise their operation for very specific conditions. However, demands for versatility, more intelligent behaviour and, in summary, an increased computing capability began to clash with these limitations, intensified by the uncertainty associated to the more dynamic operating environments where they were progressively being deployed. This brought as a result an increasing need for systems to respond by themselves to unexpected events at design time, such as: changes in input data characteristics and system environment in general; changes in the computing platform itself, e.g., due to faults and fabrication defects; and changes in functional specifications caused by dynamically changing system objectives. As a consequence, systems complexity is increasing, but in turn, autonomous lifetime adaptation without human intervention is being progressively enabled, allowing them to take their own decisions at run-time. This type of systems is known, in general, as selfadaptive, and are able, among others, of self-configuration, self-optimisation and self-repair. Traditionally, the soft part of a system has mostly been so far the only place to provide systems with some degree of adaptation capabilities. However, the performance to power ratios of software driven devices like microprocessors are not adequate for embedded systems in many situations. In this scenario, the resulting rise in applications complexity is being partly addressed by rising devices complexity in the form of multi and many core devices; but sadly, this keeps on increasing power consumption. Besides, design methodologies have not been improved accordingly to completely leverage the available computational power from all these cores. Altogether, these factors make that the computing demands new applications pose are not being wholly satisfied. The traditional solution to improve performance to power ratios has been the switch to hardware driven specifications, mainly using ASICs. However, their costs are highly prohibitive except for some mass production cases and besidesthe static nature of its structure complicates the solution to the adaptation needs. The advancements in fabrication technologies have made that the once slow, small FPGA used as glue logic in bigger systems, had grown to be a very powerful, reconfigurable computing device with a vast amount of computational logic resources and embedded, hardened signal and general purpose processing cores. Its reconfiguration capabilities have enabled software-like flexibility to be combined with hardware-like computing performance, which has the potential to cause a paradigm shift in computer architecture since hardware cannot be considered as static anymore. This is so, since, as is the case with SRAMbased FPGAs, Dynamic Partial Reconfiguration (DPR) is possible. This means that subsets of the FPGA computational resources can now be changed (reconfigured) at run-time while the rest remains active. Besides, this reconfiguration process can be triggered internally by the device itself. This technological boost in reconfigurable hardware devices is actually covered under the field known as Reconfigurable Computing. One of the most exotic fields of application that Reconfigurable Computing has enabled is the known as Evolvable Hardware (EHW), in which this dissertation is framed. The main idea behind the concept is turning hardware that is adaptable through reconfiguration into an evolvable entity subject to the forces of an evolutionary process, inspired by that of natural, biological species, that guides the direction of change. It is yet another application of the field of Evolutionary Computation (EC), which comprises a set of global optimisation algorithms known as Evolutionary Algorithms (EAs), considered as universal problem solvers. In analogy to the biological process of evolution, in EHW the subject of evolution is a population of circuits that tries to get adapted to its surrounding environment by progressively getting better fitted to it generation after generation. Individuals become circuit configurations representing bitstreams that feature reconfigurable circuit descriptions. By selecting those that behave better, i.e., with a higher fitness value after being evaluated, and using them as parents of the following generation, the EA creates a new offspring population by using so called genetic operators like mutation and recombination. As generations succeed one another, the whole population is expected to approach to the optimum solution to the problem of finding an adequate circuit configuration that fulfils system objectives. The state of reconfiguration technology after Xilinx XC6200 FPGA family was discontinued and replaced by Virtex families in the late 90s, was a major obstacle for advancements in EHW; closed (non publicly known) bitstream formats; dependence on manufacturer tools with highly limiting support of DPR; slow speed of reconfiguration; and random bitstream modifications being potentially hazardous for device integrity, are some of these reasons. However, a proposal in the first 2000s allowed to keep investigating in this field while DPR technology kept maturing, the Virtual Reconfigurable Circuit (VRC). In essence, a VRC in an FPGA is a virtual layer acting as an application specific reconfigurable circuit on top of an FPGA fabric that reduces the complexity of the reconfiguration process and increases its speed (compared to native reconfiguration). It is an array of computational nodes specified using standard HDL descriptions that define ad-hoc reconfigurable resources; routing multiplexers and a set of configurable processing elements, each one containing all the required functions, which are selectable through functionality multiplexers as in microprocessor ALUs. A large register acts as configuration memory, so VRC reconfiguration is very fast given it only involves writing this register, which drives the selection signals of the set of multiplexers. However, large overheads are introduced by this virtual layer; an area overhead due to the simultaneous implementation of every function in every node of the array plus the multiplexers, and a delay overhead due to the multiplexers, which also reduces maximum frequency of operation. The very nature of Evolvable Hardware, able to optimise its own computational behaviour, makes it a good candidate to advance research in self-adaptive systems. Combining a selfreconfigurable computing substrate able to be dynamically changed at run-time with an embedded algorithm that provides a direction for change, can help fulfilling requirements for autonomous lifetime adaptation of FPGA-based embedded systems. The main proposal of this thesis is hence directed to contribute to autonomous self-adaptation of the underlying computational hardware of FPGA-based embedded systems by means of Evolvable Hardware. This is tackled by considering that the computational behaviour of a system can be modified by changing any of its two constituent parts: an underlying hard structure and a set of soft parameters. Two main lines of work derive from this distinction. On one side, parametric self-adaptation and, on the other side, structural self-adaptation. The goal pursued in the case of parametric self-adaptation is the implementation of complex evolutionary optimisation techniques in resource constrained embedded systems for online parameter adaptation of signal processing circuits. The application selected as proof of concept is the optimisation of Discrete Wavelet Transforms (DWT) filters coefficients for very specific types of images, oriented to image compression. Hence, adaptive and improved compression efficiency, as compared to standard techniques, is the required goal of evolution. The main quest lies in reducing the supercomputing resources reported in previous works for the optimisation process in order to make it suitable for embedded systems. Regarding structural self-adaptation, the thesis goal is the implementation of self-adaptive circuits in FPGA-based evolvable systems through an efficient use of native reconfiguration capabilities. In this case, evolution of image processing tasks such as filtering of unknown and changing types of noise and edge detection are the selected proofs of concept. In general, evolving unknown image processing behaviours (within a certain complexity range) at design time is the required goal. In this case, the mission of the proposal is the incorporation of DPR in EHW to evolve a systolic array architecture adaptable through reconfiguration whose evolvability had not been previously checked. In order to achieve the two stated goals, this thesis originally proposes an evolvable platform that integrates an Adaptation Engine (AE), a Reconfiguration Engine (RE) and an adaptable Computing Engine (CE). In the case of parametric adaptation, the proposed platform is characterised by: • a CE featuring a DWT hardware processing core adaptable through reconfigurable registers that holds wavelet filters coefficients • an evolutionary algorithm as AE that searches for candidate wavelet filters through a parametric optimisation process specifically developed for systems featured by scarce computing resources • a new, simplified mutation operator for the selected EA, that together with a fast evaluation mechanism of candidate wavelet filters derived from existing literature, assures the feasibility of the evolutionary search involved in wavelets adaptation In the case of structural adaptation, the platform proposal takes the form of: • a CE based on a reconfigurable 2D systolic array template composed of reconfigurable processing nodes • an evolutionary algorithm as AE that searches for candidate configurations of the array using a set of computational functionalities for the nodes available in a run time accessible library • a hardware RE that exploits native DPR capabilities of FPGAs and makes an efficient use of the available reconfigurable resources of the device to change the behaviour of the CE at run time • a library of reconfigurable processing elements featured by position-independent partial bitstreams used as the set of available configurations for the processing nodes of the array Main contributions of this thesis can be summarised in the following list. • An FPGA-based evolvable platform for parametric and structural self-adaptation of embedded systems composed of a Computing Engine, an evolutionary Adaptation Engine and a Reconfiguration Engine. This platform is further developed and tailored for both parametric and structural self-adaptation. • Regarding parametric self-adaptation, main contributions are: – A CE adaptable through reconfigurable registers that enables parametric adaptation of the coefficients of an adaptive hardware implementation of a DWT core. – An AE based on an Evolutionary Algorithm specifically developed for numerical optimisation applied to wavelet filter coefficients in resource constrained embedded systems. – A run-time self-adaptive DWT IP core for embedded systems that allows for online optimisation of transform performance for image compression for specific deployment environments characterised by different types of input signals. – A software model and hardware implementation of a tool for the automatic, evolutionary construction of custom wavelet transforms. • Lastly, regarding structural self-adaptation, main contributions are: – A CE adaptable through native FPGA fabric reconfiguration featured by a two dimensional systolic array template of reconfigurable processing nodes. Different processing behaviours can be automatically mapped in the array by using a library of simple reconfigurable processing elements. – Definition of a library of such processing elements suited for autonomous runtime synthesis of different image processing tasks. – Efficient incorporation of DPR in EHW systems, overcoming main drawbacks from the previous approach of virtual reconfigurable circuits. Implementation details for both approaches are also originally compared in this work. – A fault tolerant, self-healing platform that enables online functional recovery in hazardous environments. The platform has been characterised from a fault tolerance perspective: fault models at FPGA CLB level and processing elements level are proposed, and using the RE, a systematic fault analysis for one fault in every processing element and for two accumulated faults is done. – A dynamic filtering quality platform that permits on-line adaptation to different types of noise and different computing behaviours considering the available computing resources. On one side, non-destructive filters are evolved, enabling scalable cascaded filtering schemes; and on the other, size-scalable filters are also evolved considering dynamically changing computational filtering requirements. This dissertation is organized in four parts and nine chapters. First part contains chapter 1, the introduction to and motivation of this PhD work. Following, the reference framework in which this dissertation is framed is analysed in the second part: chapter 2 features an introduction to the notions of self-adaptation and autonomic computing as a more general research field to the very specific one of this work; chapter 3 introduces evolutionary computation as the technique to drive adaptation; chapter 4 analyses platforms for reconfigurable computing as the technology to hold self-adaptive hardware; and finally chapter 5 defines, classifies and surveys the field of Evolvable Hardware. Third part of the work follows, which contains the proposal, development and results obtained: while chapter 6 contains an statement of the thesis goals and the description of the proposal as a whole, chapters 7 and 8 address parametric and structural self-adaptation, respectively. Finally, chapter 9 in part 4 concludes the work and describes future research paths.

3D reconstruction of long bones utilising magnetic resonance imaging (MRI)

The design of pre-contoured fracture fixation implants (plates and nails) that correctly fit the anatomy of a patient utilises 3D models of long bones with accurate geometric representation. 3D data is usually available from computed tomography (CT) scans of human cadavers that generally represent the above 60 year old age group. Thus, despite the fact that half of the seriously injured population comes from the 30 year age group and below, virtually no data exists from these younger age groups to inform the design of implants that optimally fit patients from these groups. Hence, relevant bone data from these age groups is required. The current gold standard for acquiring such data–CT–involves ionising radiation and cannot be used to scan healthy human volunteers. Magnetic resonance imaging (MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the long bones. However, in order to use MRI effectively for 3D reconstruction of human long bones, further validations using long bones and appropriate reference standards are required. Accurate reconstruction of 3D models from CT or MRI data sets requires an accurate image segmentation method. Currently available sophisticated segmentation methods involve complex programming and mathematics that researchers are not trained to perform. Therefore, an accurate but relatively simple segmentation method is required for segmentation of CT and MRI data. Furthermore, some of the limitations of 1.5T MRI such as very long scanning times and poor contrast in articular regions can potentially be reduced by using higher field 3T MRI imaging. However, a quantification of the signal to noise ratio (SNR) gain at the bone - soft tissue interface should be performed; this is not reported in the literature. As MRI scanning of long bones has very long scanning times, the acquired images are more prone to motion artefacts due to random movements of the subject‟s limbs. One of the artefacts observed is the step artefact that is believed to occur from the random movements of the volunteer during a scan. This needs to be corrected before the models can be used for implant design. As the first aim, this study investigated two segmentation methods: intensity thresholding and Canny edge detection as accurate but simple segmentation methods for segmentation of MRI and CT data. The second aim was to investigate the usability of MRI as a radiation free imaging alternative to CT for reconstruction of 3D models of long bones. The third aim was to use 3T MRI to improve the poor contrast in articular regions and long scanning times of current MRI. The fourth and final aim was to minimise the step artefact using 3D modelling techniques. The segmentation methods were investigated using CT scans of five ovine femora. The single level thresholding was performed using a visually selected threshold level to segment the complete femur. For multilevel thresholding, multiple threshold levels calculated from the threshold selection method were used for the proximal, diaphyseal and distal regions of the femur. Canny edge detection was used by delineating the outer and inner contour of 2D images and then combining them to generate the 3D model. Models generated from these methods were compared to the reference standard generated using the mechanical contact scans of the denuded bone. The second aim was achieved using CT and MRI scans of five ovine femora and segmenting them using the multilevel threshold method. A surface geometric comparison was conducted between CT based, MRI based and reference models. To quantitatively compare the 1.5T images to the 3T MRI images, the right lower limbs of five healthy volunteers were scanned using scanners from the same manufacturer. The images obtained using the identical protocols were compared by means of SNR and contrast to noise ratio (CNR) of muscle, bone marrow and bone. In order to correct the step artefact in the final 3D models, the step was simulated in five ovine femora scanned with a 3T MRI scanner. The step was corrected using the iterative closest point (ICP) algorithm based aligning method. The present study demonstrated that the multi-threshold approach in combination with the threshold selection method can generate 3D models from long bones with an average deviation of 0.18 mm. The same was 0.24 mm of the single threshold method. There was a significant statistical difference between the accuracy of models generated by the two methods. In comparison, the Canny edge detection method generated average deviation of 0.20 mm. MRI based models exhibited 0.23 mm average deviation in comparison to the 0.18 mm average deviation of CT based models. The differences were not statistically significant. 3T MRI improved the contrast in the bone–muscle interfaces of most anatomical regions of femora and tibiae, potentially improving the inaccuracies conferred by poor contrast of the articular regions. Using the robust ICP algorithm to align the 3D surfaces, the step artefact that occurred by the volunteer moving the leg was corrected, generating errors of 0.32 ± 0.02 mm when compared with the reference standard. The study concludes that magnetic resonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models of long bones with accurate geometric representations. The method is, therefore, a potential alternative to the current gold standard CT imaging.

Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

Desenvolvimento de ferramenta computacional para metrologia com microtomografia computadorizada

Esta tese apresentada uma proposta de desenvolvimento de uma ferramenta computacional para metrologia com microtomografia computadorizada que possa ser implantada em sistemas de microtomógrafos convencionais. O estudo concentra-se nas diferentes técnicas de detecção de borda utilizadas em processamento de imagens digitais.Para compreender a viabilidade do desenvolvimento da ferramenta optou-se por utilizar o Matlab 2010a. A ferramenta computacional proposta é capaz de medir objetos circulares e retangulares. As medidas podem ser horizontais ou circulares, podendo ser realizada várias medidas de uma mesma imagem, uma medida de várias imagens ou várias medidas de várias imagens. As técnicas processamento de imagens digitais implementadas são a limiarização global com escolha do threshold manualmente baseado no histograma da imagem ou automaticamente pelo método de Otsu, os filtros de passa-alta no domínio do espaço Sobel, Prewitt, Roberts, LoG e Canny e medida entre os picos mais externos da 1 e 2 derivada da imagem. Os resultados foram validados através de comparação com os resultados de teste realizados pelo Laboratório de Ensaios Mecânicos e Metrologia (LEMec) do Intstituto Politécnico do Rio de Janeiro (IPRJ), Universidade do Estado do Rio de Janeiro (UERJ), Nova Friburdo- RJ e pelo Serviço Nacional da Indústria Nova Friburgo (SENAI/NF). Os resultados obtidos pela ferramenta computacional foram equivalentes aos obtidos com os instrumentos de medição utilizados, demonstrando à viabilidade de utilização da ferramenta computacional a metrologia.

基于Zernike正交矩的图像亚像素边缘检测算法改进

介绍了Zernike矩及基于Zernike矩的图像亚像素边缘检测原理,针对Ghosal提出的基于Zernike矩的亚像素图像边缘检测算法检测出的图像存在边缘较粗及边缘亚像素定位精度低等不足,提出了一种改进算法.推导了7×7 Zernike矩模板系数,提出一种新的边缘判断依据.改进的算法能较好检测图像边缘并实现了较高的边缘定位.最后,设计了3组不同的实验.实验结果同Canny算子及Ghosal算法相比,证明了改进算法的优越性.

Finding Edges and Lines in Images

The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argued for. This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator. Variational techniques are used to find a solution over the space of all linear shift invariant operators. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should be as close as possible to the centre of the true edge. The third criterion is that there should be low probability of more than one response to a single edge. The technique is used to find optimal operators for step edges and for extended impulse profiles (ridges or valleys in two dimensions). The extension of the one dimensional operators to two dimentions is then discussed. The result is a set of operators of varying width, length and orientation. The problem of combining these outputs into a single description is discussed, and a set of heuristics for the integration are given.

Face segregation and recognition by cortical multi-scale line and edge coding

Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

Cortical object segregation and categorization by multi-scale line and edge coding

In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the framework of a complete cortical architecture.

Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.

A subpixel edge detector applied to aortic dissection detection

[EN] The aortic dissection is a disease that can cause a deadly situation, even with a correct treatment. It consists in a rupture of a layer of the aortic artery wall, causing a blood flow inside this rupture, called dissection. The aim of this paper is to contribute to its diagnosis, detecting the dissection edges inside the aorta. A subpixel accuracy edge detector based on the hypothesis of partial volume effect is used, where the intensity of an edge pixel is the sum of the contribution of each color weighted by its relative area inside the pixel. The method uses a floating window centred on the edge pixel and computes the edge features. The accuracy of our method is evaluated on synthetic images of different hickness and noise levels, obtaining an edge detection with a maximal mean error lower than 16 percent of a pixel.

Sviluppo di un sistema per l'ottimizzazione della dose in radioterapia tramite "dose-painting" basato sui voxel

Il presente lavoro è stato svolto presso la struttura di Fisica Medica dell'Azienda Ospedaliera IRCCS "Arcispedale S. Maria Nuova" di Reggio Emilia e consiste nello sviluppo di un sistema per l'ottimizzazione della dose in Radioterapia tramite dose-painting basato sui voxel. Il dose painting è una tecnica di pianificazione del trattamento di radioterapia che si basa sull'assegnazione o ridistribuzione della dose secondo le informazioni biologiche e metaboliche che i moderni sistemi di imaging sono in grado di fornire. La realizzazione del modulo di calcolo BioOPT è stata possibile grazie all'utilizzo dei due software open-source Plastimatch e CERR, specifici per l'elaborazione e la registrazione di immagini mediche di diversi tipi e formati e per la gestione, la modifica e il salvataggio di piani di trattamento di radioterapia creati con la maggior parte dei software commerciali ed accademici. Il sistema sviluppato è in grado di registrare le immagini relative ad un paziente, in generale ottenute da diverse tipologie di imaging e acquisite in tempi diversi ed estrarre le informazioni biologiche relative ad una certa struttura. Tali informazioni verranno poi utilizzate per calcolare le distribuzioni di dose "ottimale" che massimizzano il valore delle funzioni radiobiologiche utilizzate per guidare il processo di redistribuzione della dose a livello dei voxel (dose-painting). In questo lavoro il modulo è stato utilizzato principalmente per l'ottimizzazione della dose in pazienti affetti da Glioblastoma, un tumore cerebrale maligno tra i più diffusi e mortali. L'ottimizzatore voxel-based, infatti, è stato sviluppato per essere utilizzabile all'interno di un progetto di ricerca finanziato dal Ministero della Salute per la valutazione di un programma di terapia che include l'uso di un innovativo acceleratore lineare ad alto rateo di dose per la cura di tumori cerebrali in fase avanzata. Al fine di migliorare il trattamento radiante, inoltre, lo studio include la somministrazione della dose tramite dose-painting con lo scopo di verificarne l'efficacia. I risultati ottenuti mostrano che tramite il modulo sviluppato è possibile ottenere distribuzioni di dose eterogenee che tengono conto delle caratteristiche biologiche intratumore stimate a partire dalle immagini multimodali. Inoltre il sistema sviluppato, grazie alla sua natura 'open', è altamente personalizzabile a scopi di ricerca e consente di simulare distribuzioni di dose basate sui voxel e di confrontarle con quelle ottenute con i sistemi commerciali ad uso clinico, che non consentono questo tipo di ottimizzazioni.

Local edge detectors: A substrate for fine spatial vision at low temporal frequencies in rabbit retina

Visual acuity is limited by the size and density of the smallest retinal ganglion cells, which correspond to the midget ganglion cells in primate retina and the beta- ganglion cells in cat retina, both of which have concentric receptive fields that respond at either light- On or light- Off. In contrast, the smallest ganglion cells in the rabbit retina are the local edge detectors ( LEDs), which respond to spot illumination at both light- On and light- Off. However, the LEDs do not predominate in the rabbit retina and the question arises, what role do they play in fine spatial vision? We studied the morphology and physiology of LEDs in the isolated rabbit retina and examined how their response properties are shaped by the excitatory and inhibitory inputs. Although the LEDs comprise only similar to 15% of the ganglion cells, neighboring LEDs are separated by 30 - 40 mu m on the visual streak, which is sufficient to account for the grating acuity of the rabbit. The spatial and temporal receptive- field properties of LEDs are generated by distinct inhibitory mechanisms. The strong inhibitory surround acts presynaptically to suppress both the excitation and the inhibition elicited by center stimulation. The temporal properties, characterized by sluggish onset, sustained firing, and low bandwidth, are mediated by the temporal properties of the bipolar cells and by postsynaptic interactions between the excitatory and inhibitory inputs. We propose that the LEDs signal fine spatial detail during visual fixation, when high temporal frequencies are minimal.

Third-derivative filters predict edge locations in spatial vision

Edge detection is crucial in visual processing. Previous computational and psychophysical models have often used peaks in the gradient or zero-crossings in the 2nd derivative to signal edges. We tested these approaches using a stimulus that has no such features. Its luminance profile was a triangle wave, blurred by a rectangular function. Subjects marked the position and polarity of perceived edges. For all blur widths tested, observers marked edges at or near 3rd derivative maxima, even though these were not 1st derivative maxima or 2nd derivative zero-crossings, at any scale. These results are predicted by a new nonlinear model based on 3rd derivative filtering. As a critical test, we added a ramp of variable slope to the blurred triangle-wave luminance profile. The ramp has no effect on the (linear) 2nd or higher derivatives, but the nonlinear model predicts a shift from seeing two edges to seeing one edge as the ramp gradient increases. Results of two experiments confirmed such a shift, thus supporting the new model. [Supported by the Engineering and Physical Sciences Research Council].