Multi-Object Geodesic Active Contours (MOGAC): A Parallel Sparse-Field Algorithm for Image Segmentation

An important task for computer vision systems is to segment adjacent structures in images without producing gaps or overlaps. Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel accuracy. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel accuracy. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the segmentation algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label image and unsigned distance field. The time complexity of the algorithm is shown to be O((M^d)/P) for M^d pixels and P processing units in dimension d={2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems

Multi-Object Geodesic Active Contours (MOGAC

Abstract. An emerging topic is to build image segmentation systems that can segment hundreds to thousands of objects (i.e. cell segmentation \ tracking, full brain parcellation, full body segmentation, etc.). Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel precision. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel precision. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label mask image and unsigned distance field. The time complexity of the algorithm is shown to be O((M^d)/P) for M^d pixels and P processing units in dimension d={2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems

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