47 research outputs found

    Comparison of ICP (dark) and CICP (light) in two steps with (a) average LMSD and (b) LMAXD on US-MERGE and US-SNAP datasets.

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    <p>An asterisk in (a) and (b) indicates statistically significant (<i>p</i> < 0.05) reduction in average LMSD or LMAXD from ICP to CICP.</p

    Average ΔLMSD using TACICP algorithm with different amplitude of zero mean Gaussian noise on the contours on US-MERGE and US-SNAP datasets.

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    <p>Average ΔLMSD using TACICP algorithm with different amplitude of zero mean Gaussian noise on the contours on US-MERGE and US-SNAP datasets.</p

    Comparison of correct label configuration (dark) and reverted configuration (light) with average LMSD on US-MERGE and US-SNAP datasets.

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    <p>An asterisk indicates statistically significant (<i>p</i> < 0.05) increment in average LMSD from correct configuration to reverted one.</p

    RSR<sub>1.5</sub> and computation time with the same configuration for different feature-based algorithms on US-MERGE and US-SNAP datasets.

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    <p>RSR<sub>1.5</sub> and computation time with the same configuration for different feature-based algorithms on US-MERGE and US-SNAP datasets.</p

    Overview of proposed TACICP algorithm for carotid image registration.

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    <p>The segmented 2D contours from images are the only inputs for our algorithm. Centerline and surface features are generated automatically from contours for two steps. The final output of the registration is a transformation composed by the rigid transformation <i>T</i><sub>rigid</sub> from rigid initialization step and the thin-plate-spline transformation <i>T</i><sub>TPS</sub> from non-rigid refinement step.</p
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