Segmentation of Brain Image Volumes Using the Data List Management Library

The segmentation of head images is useful to detect neuroanatomical structures and to follow and quantify the evolution of several brain lesions. 2D images correspond to brain slices. The more images are used the higher the resolution obtained is, but more processing power is required and parallelis...

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Bibliographic Details
Main Authors: Roman-Alonso, G., Jimenez-Alaniz, J.R., Buenabad-Chavez, J., Castro-Garcia, M.A., Vargas-Rodriguez, A.H.
Format: Conference Proceeding
Language:English
Subjects:
Brain
Image resolution
Image segmentation
Lesions
Libraries
Magnetic heads
Magnetic resonance imaging
Programming profession
Shape
Surges
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Description
Summary:The segmentation of head images is useful to detect neuroanatomical structures and to follow and quantify the evolution of several brain lesions. 2D images correspond to brain slices. The more images are used the higher the resolution obtained is, but more processing power is required and parallelism becomes desirable. We present a new approach to segmentation of brain image volumes using DLML (data list management library), a tool developed by our team. We organise the integer numbers identifying images into a list, and our DLML version process them both in parallel and with dynamic load balancing transparently to the programmer. We compare the performance of our DLML version to other typical parallel approaches developed with MPI (master-slave and static data distribution), using cluster configurations with 4-32 processors.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2007.4352732