Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)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. © 2009 IEEE. 126132Petrobras,CNPq,CAPES,INCTMat,FAPERJConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Broit, C., Optimal registration of deformed images, (1981), Ph.D. dissertation, Computer and Information Science Dept, University of Pennsylvania, Philadelphia, PAKapouleas, I., Segmentation and feature extraction for magnetic resonance brain image analysis (1990) Proceedings of the 10th International Conference on Pattern Recognition, 1, pp. 583-590. , 1Gerig, G., Martin, J., Kikinis, R., Kübler, O., Shenton, M.E., Jolesz, F.A., Automating segmentation of dual-echo MR head data (1991) IPMI 91: Proceedings of the 12th International Conference on Information Processing in Medical Imaging, pp. 175-187. , London, UK: Springer-VerlagBrummer, M.E., Mersereau, R.M., Eisner, R.L., Lewine, R.R., Automatic detection of brain contours in MRI data sets (1993) IEEE Transactions on Medical Imaging, 12 (2), pp. 153-166M. Miller, G. Christensen, Y. Amit, and U. Grenander, Mathematical textbook of deformable neuroanatomies, Proceedings of the National Academy of Sciences of the United States of America, 90, no. 24, pp. 11 944-11 948, 1993Collins, L.D., Holmes, C.J., Peters, T.M., Evans, A.C., Automatic 3-D model-based neuroanatomical segmentation (1995) Human Brain Mapping, 3 (3), pp. 190-208Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., Evans, A.C., ANIMAL+INSECT: Improved cortical structure segmentation (1999) IPMI 99: Proceedings of the 16th International Conference on Information Processing in Medical Imaging, pp. 210-223. , London, UK: Springer-VerlagZhukov, L., Museth, K., Breen, D., Whitaker, R., Barr, A., Level set modeling and segmentation of DT-MRI brain data (2003) Journal of Electronic Imaging, 12, pp. 125-133Wang, Z., Vemuri, B., DTI segmentation using an information theoretic tensor dissimilarity measure (2005) IEEE Transactions on Medical ImagingJonasson, L., Bresson, X., Hagmann, P., Cuisenaire, O., Meuli, R., Thiran, J., White matter fiber tract segmentation in DT-MRI using geometric flows (2005) Medical Image Analysis, 9 (3), pp. 223-236Weldeselassie, Y., Hamarneh, G., DT-MRI segmentation using graph cuts (2007) Medical Imaging 2007: Image Processing, Proceedings of the SPIE, , SPIEY. Y. Boykov and M.-P. Jolly, Interactive graph cuts for optimal boundary and region segmentation of objects in ND images, International Conference in Computer Vision, 01, p. 105, 2001Lenglet, C., Rousson, M., Deriche, R., A statistical framework for DTI segmentation (2006) Proceedings of the International Symposium on Biomedical Imaging, pp. 794-797. , IEEENiogi, S.N., Mukherjee, P., McCandliss, B.D., Diffusion tensor imaging segmentation of white matter structures using a reproducible objective quantification scheme (ROQS) (2007) NeuroImage, 35, pp. 166-174Rittner, L., Flores, F., Lotufo, R., New tensorial representation of color images: Tensorial morphological gradient applied to color image segmentation (2007) SIBGRAPI07: Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, pp. 45-52. , Belo Horizonte, Brazil: IEEE Computer SocietyRittner, L., Lotufo, R., Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient (2008) SIBGRAPI08: Proceedings of the XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 196-203. , Campo Grande, Brazil: IEEE Computer SocietyHeijmans, H.J.A.M., (1994) Morphological Image Operators, , Boston: Academic PressPierpaoli, C., Basser, P.J., Toward a quantitative assessment of diffusion anisotropy (1996) Magnetic Resonance in Medicine, 36 (6), pp. 893-906Alexander, D., Gee, J., Bajcsy, R., Similarity measures for matching diffusion tensor images Proceedings of the British Machine Vision Conference, 1, pp. 93-102. , Nottingham, UK, ppJones, A.S.D.K., Horsfield, M.A., Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging (1999) Magnetic Resonance in Medicine, 42 (3), pp. 515-525Wiegell, M., Tuch, D., Larson, H., Wedeen, V., Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging (2003) NeuroImage, 19, pp. 391-402Ziyan, U., Tuch, D., Westin, C., Segmentation of thalamic nuclei from DTI using spectral clustering (2006) ser. Lecture Notes in Computer Science, pp. 807-814. , MICCAI06: Proceedings of the Medical Image Computing and Computer Assisted Intervention, DenmarkBeucher, S., Meyer, F., The morphological approach to segmentation: The watershed transformation (1992) Mathematical Morphology in Image Processing, pp. 433-481. , Marcel Dekker, ch. 12, ppVincent, L., Soille, P., Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations (1991) IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598. , JuneMeyer, F., An overview of morphological segmentation (2001) International Journal of Pattern Recognition and Artificial Intelligence, 15 (7), pp. 1089-1118Dougherty, E.R., Lotufo, R.A., (2003) Hands-on Morphological Image Processing, TT59. , SPIE