%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2009/08.21.14.18
%2 sid.inpe.br/sibgrapi@80/2009/08.21.14.18.55
%@doi 10.1109/SIBGRAPI.2009.36
%T Segmentation of brain structures by watershed transform on tensorial morphological gradient of diffusion tensor imaging
%D 2009
%A Rittner, Leticia,
%A Appenzeller, Simone,
%A Lotufo, Roberto de Alencar,
%@affiliation Faculdade de Engenharia Elétrica - UNICAMP
%@affiliation Faculdade de Medicina - UNICAMP
%@affiliation Faculdade de Engenharia Elétrica - UNICAMP
%E Nonato, Luis Gustavo,
%E Scharcanski, Jacob,
%B Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
%C Rio de Janeiro, RJ, Brazil
%8 11-14 Oct. 2009
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Diffusion tensor imaging, MRI, Segmentation, Watershed transform, Mathematical morphology.
%X Watershed transform on tensorial morphological gradient (TMG) is a new approach to segment diffusion tensor images (DTI). Since the TMG is able to express the tensorial dissimilarities in a single scalar image, the segmentation problem of DTI is then reduced to a scalar image segmentation problem. Therefore, it can be addressed by well-known segmentation techniques, such as the watershed transform. In other words, by computing the TMG of a DTI, and then using the hierarchical watershed transform, it is possible to segment brain structures, such as the corpus callosum, the ventricles and the cortico-spinal tracts, and use the results for subsequent quantitative analysis of DTI parameters. Experiments showed that segmentations obtained with the proposed approach are similar to the ones obtained by other segmentation techniques based on DTI and also segmentation methods based on other Magnetic Resonance Imaging (MRI) modalities. Since the proposed method, as opposed to the majority of the DTI based segmentation methods, does not require manual seed and/or surface placement, its results are highly repeatable. And unlike other methods that have sometimes four parameters to be adjusted, the only adjustable parameter is the number of regions in which the image should be segmented, making it simple and robust.
%@language en
%3 PID950047.pdf