Integrated multimedia electronic patient record and graph-based image information for cerebral tumors.

Current electronic patient record (EPR) implementations do not incorporate medical images, nor structural information extracted from them, despite images increasing role for diagnosis. This paper presents an integration framework into EPRs of anatomical and pathological knowledge extracted from segmented magnetic resonance imaging (MRI), applying a graph of representation for anatomical and functional information for individual patients. Focusing on cerebral tumors examination and patient follow-up, multimedia EPRs were created and evaluated through a 3D navigation application, developed with open-source libraries and standards. Results suggest that the enhanced clinical information scheme could lead to original changes in the way medical experts utilize image-based information.

Combining radiometric and spatial structural information in a new metric for minimal surface segmentation.

Segmentation of anatomical structures via minimal surface extraction using gradient-based metrics is a popular approach, but exhibits some limits in the case of weak or missing contour information. We propose a new framework to define metrics, robust to missing image information. Given an object of interest we combine gray-level information and knowledge about the spatial organization of cerebral structures, into a fuzzy set which is guaranteed to include the object's boundaries. From this set we derive a metric which is used in a minimal surface segmentation framework. We show how this metric leads to improved segmentation of subcortical gray matter structures. Quantitative results on the segmentation of the caudate nucleus in T1 MRI are reported on 18 normal subjects and 6 pathological cases.