Abstract. This paper describes an automatic tissue segmentation method for neonatal MRI. The analysis and study of neonatal brain MRI is of great interest due to its potential for studying early growth patterns and morphologic change in neurodevelopmental disorders. Automatic segmentation

This paper presents our recent study to evaluate how effectively the image texture information within the hippocampus structure can help the physicians to determine the candidates for epilepsy surgery. First we segment the hippocampus from T1-weighted images using our newly developed knowledge

remains problematic given the lack of a suitable IT infrastructure within many institutions and the problems associated with sharing imaging data in a secure and controlled way. Grid computing offers a potential solution to such problems through the promise of large scale distributed resource sharing

et al., Hippocampus 1998;8:313-- 322) that appears to contradict the fMRI encoding data. Based on their analysis of the rostrocaudal distribution of activations reported during episodic encoding or retrieval, Lepage et al. (1998) concluded that anterior MTL is strongly associated with episodic

Segmented analysis of subcortical structures within the nonhuman primate can potentially have a profound impact on studying the relationship between volumetric characteristics and alcohol dependencies. Image segmentations have been widely used in quantifying structural information. There are a

. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing

of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra-high resolution MRI data in a fully

in this paper: they rely on data and sources presented in the updated version of my trilogy (Castells 2000a). For the sake of clarity, I will rst present the conceptual framework I use in my analysis of social structure. I will then proceed to enumerate the main transformations taking place in social structures

of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra-high resolution MRI data in a fully

Automatic brain MRI segmentations methods are useful but computationally intensive tools in medical image computing. Deploying them on grid infrastructures can provide an efficient resource for data handling and computing power. In this study, an efficient implementation of a brain MRI segmentation