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19 March 2008 Efficient curvature estimations for real-time (25Hz) segmentation of volumetric ultrasound data
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Abstract
While Moore's law has eliminated the need for algorithm optimization when computing 2D dynamic contours, real-time 3D image analysis remains limited by computational bottlenecks. We are specifically concerned with segmenting 3D volumetric ultrasound streams from echo-cardiograph machines (Phillips Medical Systems, Andover, MA) for analysis of cardiac function. The system uses a 3000 element array that produces 20-25 volumes per second at a resolution of 128x48x204 voxels. This yields a data rate of of 240 Mbits/sec, requiring efficient algorithms and implementations to track moving cardiac tissue in real-time. This paper discusses implementation of active 2D deformable models for real-time volumetric segmentation at the high data rates described above. We demonstrate that using an efficient approximation of local curvature change in the implementation of dynamic contours leads to real-time volumetric segmentation on mid-range off-the-shelf hardware without the use of specialized graphics hardware. Our dynamic contour implementation relies on an optimal estimation of local curvature change based on a Menger curvature calculation. We investigate the role of curvature approximations and smoothness with respect to optimal contour point motions and step size in real-time implementations. This smoothness provides reasonable shape estimates in the absence of appropriate or conflicting external image input. Finally, we present a 3D image segmentation algorithm based on an efficient implementation of 2D dynamic contours, and demonstrate real-time performance with high volumetric data rates.
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Christopher R. Wagner and Douglas P. Perrin "Efficient curvature estimations for real-time (25Hz) segmentation of volumetric ultrasound data", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69144H (19 March 2008); https://doi.org/10.1117/12.773015
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