Difference between revisions of "Projects:ExpectationMaximizationSegmentation"
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| − | = Expectation Maximization Segmentation of MRI Images = | + | = Robust Expectation Maximization Segmentation of MRI Images = |
Segmentation algorithms based on the Expectation Maximization (EM) theory | Segmentation algorithms based on the Expectation Maximization (EM) theory | ||
Revision as of 23:08, 23 April 2009
Home < Projects:ExpectationMaximizationSegmentationRobust Expectation Maximization Segmentation of MRI Images
Segmentation algorithms based on the Expectation Maximization (EM) theory have proved themselves capable of results of an exceptional quality. Generally such results were obtained by carefully optimizing the parameters for a specific MRI protocol and a specific anatomical region. Besides the segmentation of a standard size MRI scan often requires a processing time in the order of minutes or hours. Because of these contraints, EM algorithms have found a limited usability in the clinical environment. Our project aims at addressing these issues and designing a new framework that would be easily trackable by a clinician. The background of our team encompasses Computer Science and Radiology, as well as Research and Industry. Our focus will be to identify the bottle necks of existing EM algorithms, validate that our method outperfoms existing solutions, and provide an intuitive implementation to the Research and Clinical Community,
Key Investigators
- Sylvain Jaume, MIT
- Koen Van Leemput, MGH
- Polina Golland, MIT
- Ron Kikinis, BWH
- Steve Pieper, BWH
