Diffusion Tensor Imaging (DTI) and Morphological Processing
Diffusion tensor imaging (DTI) is a relatively new MRI modality capable of generating contrasts that are sensitive to fi ber orientations. It carries rich information about intra-white-matter axonal anatomy, which cannot be seen in conventional MRI. DTI-based segmentation, where regions of interest are delineated, is necessary for performing subsequent quantitative analysis and qualitative visualization. While scalar image segmentation has been studied extensively and different algorithms have been developed over the last decades, DTI-based segmentation is a relatively new and challenging task.
DTI-based segmentation using TMG
The computation of a tensorial morphological gradient (TMG) is a technique proposed to transform the diffusion tensor image into a scalar map with meaningful values at edges of structures whose segmentation is desired. The TMG uses diffusion intervoxel measures and combines them to compute a gradient using concepts from mathematical morphology ( see details).
Corpus Calosum parcellation
The corpus calosum (CC) is an important brain structure for clinical studies. Some studies require the CC to be divided in 5 regions. This division is usually made based on a theoretical scheme and on atlases. The segmentation method based on the TMG and the watershed transform has a great potential to find these 5 regions ( see details).
Thalamic nuclei segmentation
Although thalamic nuclei are not directly visible on conventional anatomical magnetic resonance images (MRI), it is possible to observe differences between the nuclei using diffusion tensor imaging (DTI), because of their distinct fiber orientation. This project searchs a method to segment the various nuclei of human thalamus using diffusion MRI. Our approach is to use the watershed transform and other concepts from mathematical morphology to segment the nuclei ( see details).
MM-DTI - DTI Visualisation and Segmentation tool
MM-DTI is a visualization and segmentation tool for diffusion tensor images. The MM-DTI visualization approach, which separates the data information in two independent groups, named color mapping and viewing modes, allows the user to choose the best combination of visual information for each study case. In the segmentation functionality, the proposed method requires no manual seed placement and/or initial surface delineation, and is possible to control the number of regions into which the image should be segmented. Finally, the tensor animation mode, which provides the possibility of dynamically viewing a DT field, renders a digital representation of the data closer to the diffusion phenomenon ( see details)
MM-DTI in Adessowiki
MM-DTI is a visualization and segmentation tool for diffusion tensor images. It was developed in C++ and uses OpenGL.
DTI processing Toolbox in Adessowiki
DTI pipeline ( see details)