A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same

of images acquired from older children and adults. We sought to develop a fully automated segmentation strategy and present here a Bayesian approach utilizing an atlas of priors derived from previous segmentations and a new scheme for automatically selecting and iteratively refining classifier training data

Quantitative brain tissue segmentation from newborn MRI offers the possibility of improved clin-ical decision making and diagnosis, new insight into the mechanisms of disease, and new methods for the evaluation of treatment protocols for preterm newborns. Such segmentation is challenging, however

of existing structures not explained by the model. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension

, we propose an extension of group LASSO that permits associations between features within (predefined) groups to be modeled. Integrating this new penalty into classifier learning enables incorporation of additional prior information beyond group structure. In the context of fMRI, GGSC provides a

We introduce an automated segmentation method, extended Markov Random Field (eMRF) to classify 21 neuroanatomical structures of mouse brain based on three dimensional (3D) magnetic resonance imaging (MRI). The image data are multispectral: T2-weighted, proton density-weighted, diffusion x, y and z

of existing structures. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). We aim at both obtaining a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension to an existing

for an improved voxel-byvoxel normalization for functional MRI and lesion analysis. Alignment to the template requires that the cerebellum and brainstem are isolated from the surrounding tissue, a process for which an automated algorithm has been developed. Compared to normalization to the MNI whole-brain

such models is to obtain an accurate segmentation of in-dividual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available auto-mated segmentation tools only provide results for brain tissues

) and sometimes pathological tissues like tumor etc. Probabilistic Neural Network with radial basis function will be employed to implement an automated Brain Tumor classification. Decision making was performed in two stages: feature extraction using GLCM and PCA and the classification