Inferring brain tissue composition and microstructure via MR relaxometry

Brain tissue MR-image segmentation via optimum-path forest clustering

We present an accurate and fast approach for MR-image segmentation of brain tissues, that is robust to anatomical variations and takes an average of less than one minute for completion on modern PCs. The method first corrects voxel values in the brain based on local estimations of the whitematter intensities. This strategy is inspired by other works, but it is simple, fast, and very effective. ...

Tissue microstructure characterisation through relaxometry and diffusion MRI using sparse component analysis

We introduce a new approach to characterise brain tissue microstructure combining diffusion MRI and relaxometry. The tissue is modelled as a composition of water pools with distinct T2 and diffusivities. Mathematically, we establish a blind source separation framework that we solve using sparse component analysis. Results show that this technique is able to extract distinct diffusion and relaxo...

Inferring the Brain Connectivitiy from MR Diffusion Tensor Data

Segmentation of Brain Tissue from MR Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. In this paper, we present a method for segmentation of a particularly complex structure, the brain tissue, from MRI. Our method is a combination of three existing techniques from the Compu...

Brain tissue segmentation of fetal MR images

We present a segmentation method for fetal brain tissues of T2w MR images, based on the well known Expectation Maximization Markov Random Field (EM-MRF) scheme. Our main contribution is an intensity model composed of 7 Gaussian distribution designed to deal with the large intensity variability of fetal brain tissues. The second main contribution is a 3-steps MRF model that introduces both local...