Diseño de filtros digitales FIR mediante técnicas de computación evolutiva y estudio de su aplicación al procesado de señales biomédicas

El diseño de filtros digitales eficientes es una rama esencial del procesado de señales. Los filtros FIR son empleados en numerosas aplicaciones debido a su naturaleza de fase lineal y estabilidad frecuencial. Los métodos de diseño tradicionales sufren el problema del escaso control sobre la respuesta en frecuencia del filtro diseñado. Por esto, en este documento, se presenta una técnica de optimización novedosa, denominada Algoritmo de Polinización de Flores (FPA), junto con una novedosa función de aptitud múltiple, para la obtención del filtro FIR deseado. El algoritmo FPA se basa en el proceso de polinización de las flores. Dadas las especificaciones del filtro FIR, el algoritmo FPA obtiene un conjunto de coeficientes óptimos del filtro que mejor se aproxima a las especificaciones ideales. Los resultados obtenidos se han comparado con los métodos tradicionales de enventanado y algoritmo Parks-MacClellan (PM) y con otros métodos algorítmicos. Estos resultados numéricos muestran la superioridad del método de computación natural (FPA), junto con la función de aptitud múltiple en el diseño de filtros FIR paso bajo, paso alto, paso banda y elimina banda. Concretamente: Se consigue un mejor ajuste a las especificaciones del filtro deseado, una mayor atenuación de la banda eliminada y menor ancho de banda de transición a costa de aumentar ligeramente el rizado en la banda de paso.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

Optimisation of image processing networks for neuronal membrane detection

This research dealt with the problem of neuronal membrane detection, in which the core challenge is distinguishing membranes from organelles. A simple and efficient optimisation framework is proposed based on several basic processing steps, including local contrast enhancement, denoising, thresholding, hole-filling, watershed segmentation, and morphological operations. The two main algorithms proposed Image Processing Chain Optimisation (IPCO) and Multiple IPCO (MIPCO)combine elements of Genetic Algorithms, Differential Evolution, and Rank-based uniform crossover. 91.67% is the highest recorded individual IPCO score with a speed of 280 s, and 92.11% is the highest recorded ensembles IPCO score whereas 91.80% is the highest recorded individual MIPCO score with a speed of 540 s for typically less than 500 optimisation generations and 92.63% is the highest recorded ensembles MIPCO score.Further, IPCO chains and MIPCO networks do not require specialised hardware and they are easy to use and deploy. This is the first application of this approach in the context of the Drosophila first instar larva ventral nerve cord. Both algorithms use existing image processing functions, but optimise the way in which they are configured and combined. The approach differs from related work in terms of the set of functions used, the parameterisations allowed, the optimisation methods adopted, the combination framework, and the testing and analyses conducted. Both IPCO and MIPCO are efficient and interpretable, and facilitate the generation of new insights. Systematic analyses of the statistics of optimised chains were conducted using 30 microscopy slices with corresponding ground truth. This process revealed several interesting and unconventional insights pertaining to preprocessing, classification, post-processing, and speed, and the appearance of functions in unorthodox positions in image processing chains, suggesting new sets of pipelines for image processing. One such insight revealed that, at least in the context of our membrane detection data, it is typically better to enhance, and even classify, data before denoising them

Optimisation of image processing networks for neuronal membrane detection

This research dealt with the problem of neuronal membrane detection, in which the core challenge is distinguishing membranes from organelles. A simple and efficient optimisation framework is proposed based on several basic processing steps, including local contrast enhancement, denoising, thresholding, hole-filling, watershed segmentation, and morphological operations. The two main algorithms proposed Image Processing Chain Optimisation (IPCO) and Multiple IPCO (MIPCO)combine elements of Genetic Algorithms, Differential Evolution, and Rank-based uniform crossover. 91.67% is the highest recorded individual IPCO score with a speed of 280 s, and 92.11% is the highest recorded ensembles IPCO score whereas 91.80% is the highest recorded individual MIPCO score with a speed of 540 s for typically less than 500 optimisation generations and 92.63% is the highest recorded ensembles MIPCO score.Further, IPCO chains and MIPCO networks do not require specialised hardware and they are easy to use and deploy. This is the first application of this approach in the context of the Drosophila first instar larva ventral nerve cord. Both algorithms use existing image processing functions, but optimise the way in which they are configured and combined. The approach differs from related work in terms of the set of functions used, the parameterisations allowed, the optimisation methods adopted, the combination framework, and the testing and analyses conducted. Both IPCO and MIPCO are efficient and interpretable, and facilitate the generation of new insights. Systematic analyses of the statistics of optimised chains were conducted using 30 microscopy slices with corresponding ground truth. This process revealed several interesting and unconventional insights pertaining to preprocessing, classification, post-processing, and speed, and the appearance of functions in unorthodox positions in image processing chains, suggesting new sets of pipelines for image processing. One such insight revealed that, at least in the context of our membrane detection data, it is typically better to enhance, and even classify, data before denoising them