Digital Image-based Elasto-tomography (DIET) is an emerging method for noninvasivebreast cancer screening. Effective clinical application of the DIET systemrequires highly accurate motion tracking of the surface of an actuated breast withminimal computation. Normalized cross correlation (NCC) is the most robustcorrelation measure for determining similarity between points in two or more imagesproviding an accurate foundation for motion tracking. However, even using fastfourier transform (FFT) methods, it is too computationally intense for rapidlymanaging several large images. A significantly faster method of calculating the NCCis presented that uses rectangular approximations in place of randomly placedlandmark points or the natural marks on the breast. These approximations serve as anoptimal set of basis functions that are automatically detected, dramatically reducingcomputational requirements. To prove the concept, the method is shown to be 37-150times faster than the FFT-based NCC with the same accuracy for simulated data, avisco-elastic breast phantom experiment and human skin. Clinically, this approachenables thousands of randomly placed points to be rapidly and accurately trackedproviding high resolution for the DIET system
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