RESUMO
Several virtual research environments (VREs) have been developed to address specific tasks or application domains. Building on the experiences and use cases coming out of these projects, this paper addresses the creation of more general-purpose VREs for the humanities, which move beyond specific, focused tasks, and instead provide services and environments that support more general-purpose humanities research activities. Specifically, we are investigating use cases related to the organization and integration of the dispersed and heterogeneous information on which such research is based. These use cases are highly interactive, interpretative and researcher centric, addressing topics such as annotation environments and support for 'active-reading' processes and scholarly dialogues. We present the background to our work and the technical approach taken, and report the results obtained so far.
RESUMO
In this paper we explore a method of segmentation of muscle interstitial adipose tissue (IAT) in MR images of the thigh. The objective is to apply the method towards research into biomarkers of osteoarthritis (OA). T1-weighted images of the thigh are intensity standardized through bias field correction and intensity normalization. IAT within the thigh muscles is then segmented using a threshold combined with morphological constraints applied on connected regions in the thresholded image. The morphological constraints can be adjusted to allow for highly sensitive or highly specific IAT segmentation. The use of the morphological constraints improved the specificity of IAT segmentation over a threshold segmentation method from 0.54 to 0.67, while retaining a nearly equivalent sensitivity of 0.82 compared to 0.84. We then present a preliminary statistical analysis to demonstrate the application of the automated IAT segmentation. Finally, we specify a protocol for further exploration of IAT by leveraging the massive imaging dataset of the Osteoarthritis Initiative (OAI).