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11 March 2011 Distance transforms in multi channel MR image registration
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621D (2011)
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Deformable registration techniques play vital roles in a variety of medical imaging tasks such as image fusion, segmentation, and post-operative surgery assessment. In recent years, mutual information has become one of the most widely used similarity metrics for medical image registration algorithms. Unfortunately, as a matching criteria, mutual information loses much of its effectiveness when there is poor statistical consistency and a lack of structure. This is especially true in areas of images where the intensity is homogeneous and information is sparse. Here we present a method designed to address this problem by integrating distance transforms of anatomical segmentations as part of a multi-channel mutual information framework within the registration algorithm. Our method was tested by registering real MR brain data and comparing the segmentation of the results against that of the target. Our analysis showed that by integrating distance transforms of the the white matter segmentation into the registration, the overall segmentation of the registration result was closer to the target than when the distance transform was not used.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min Chen, Aaron Carass, John Bogovic, Pierre-Louis Bazin, and Jerry L. Prince "Distance transforms in multi channel MR image registration", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621D (11 March 2011);

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