Multimodality and Nonrigid Image Registration with Application to Diffusion Tensor Imaging

The great challenge in image registration is to devise computationally efficient algorithms for aligning images so that their details overlap accurately. The first problem addressed in this thesis is multimodality medical image registration, which we formulate as an optimization problem in the information-theoretic setting. We introduce a viable and practical image registration method by maximizing a generalized entropic dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The feasibility of the proposed image registration approach is demonstrated through extensive experiments. The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein, we extend our nonrigid registration approach to align diffusion tensor images for multiple components by enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image registration indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information, not only in terms of registration accuracy in the presence of geometric distortions but also in terms of robustness in the presence of Rician noise

Análisis de la señal de electroencefalograma mediante distancias espectrales para la ayuda en el diagnóstico de la enfermedad de Alzheimer

En este Trabajo Fin de Máster se ha estudiado la actividad electroencefalográfica (EEG) espontánea en 32 pacientes con la enfermedad de Alzheimer (EA) y en 25 sujetos de control de edad avanzada, utilizando métodos espectrales de procesado de señal. El objetivo de este estudio es determinar si la conectividad cerebral en los registros EEG presenta diferencias entre los enfermos y los controles. Para ello, se ha realizado un análisis de conectividad de la actividad espectral en los distintos sensores del EEG. Los resultados obtenidos muestran que la EA provoca una disminución de la conectividad entre neuronas alejadas entre sí. Mientras que el impacto de la demencia es menor en la conectividad de neuronas en regiones adyacentes o en una misma región cerebral. En conclusión, los resultados obtenidos muestran la capacidad de las distancias espectrales para caracterizar la similitud en el espectro de registros de EEG, así como las alteraciones en los patrones de conectividad provocados por la EA.Teoría de la Señal y las Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione