We plan to develop a novel software environment that will allow us to prepare magnetic resonance images (MRI) and computer assisted tomograms (CT) for quantitative image analysis and for intraoperative guidance. In order to accomplish our goals, we need to: consolidate already available image processing software infrastructure with the high performance computational hardware and networking facilities; improve and optimize our operator controlled segmentation environment (OCS); develop existing signal intensity based segmentation (SIS); develop existing template driven segmentation algorithms (TDS); develop an objective methodology for validation of the algorithms. OCS tools will include the development of a volume editor (VE). The second, SIS tools, will include the addition of Markov Random Fields (MRF) and Mean Fields (MF) to be added and integrated with our current expectation maximization (EM) segmenter. MRF will allow us to more fully exploit the image data by letting neighboring voxels influence the decision about the tissue class of a given voxel. This will be particularly helpful in brain regions where there is overlap between tissue classes. The inclusion of MF will enable larger neighborhoods to be taken into account in making these determinations. Thus both MRF and MF will serve as boosters to our current EM segmenter. And third, TDS tools will be developed in order to identify neuroanatomical regions of interest both quickly and more precisely. Here, we will build upon our existing brain atlas data set by adding more regions of interest. We will also use linear registration techniques as a first step in registering new brains to our atlas. The next step will involve elastic registration which is currently available in a single processor version, but will be migrated to our parallel processing machines in order to increase the number of cases that can be completed in the shortest period of time. In addition, because the brain atlas is based on a normal human brain, we will develop tools which will be used to identify, and to measure, the size and location of abnormal tissue such as brain tumors.
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