Técnicas para la segmentación y visualización eficiente de imagen médica 3D: explotando la arquitectura de la GPU

El objetivo del trabajo realizado en esta Tesis de Doctorado es proponer soluciones eficientes para el procesado de imagen médica en tarjetas gráficas (GPU). En particular, el trabajo se ha centrado en las tareas de segmentación y visualización. Ambas tareas son bastante amplias, y en la literatura es posible encontrar multitud de soluciones diferentes para cada una de ellas. Es por esto que hemos seleccionado una serie de algoritmos cuya efectividad ya está demostrada y hemos aplicado diversas de técnicas para implementarlos en GPU tratando de maximizar el rendimiento

GPU-accelerated level-set segmentation

The level-set method, a technique for the computation of evolving interfaces, is a solution commonly used to segment images and volumes in medical applications. GPUs have become a commodity hardware with hundreds of cores that can execute thousands of threads in parallel, and they are nowadays ideal platforms to execute computational intensive tasks, such as the 3D level-set-based segmentation, in real time. In this paper, we propose two GPU implementations of the level-set-based segmentation method called Fast Two-Cycle. Our proposals perform computations in independent domains called tiles and modify the structure of the original algorithm to better exploit the features of the GPU. The implementations were tested with real images of brain vessels and a synthetic MRI image of the brain. Results show that they execute faster than a CPU-sequential implementation of the same method, without any significant loss of the segmentation quality and without requiring distributed parallel computer infrastructures