Breast image registration by using non-linear local affine transformation
Feiyu Chen, Peng Zheng, Penglong Xu, Andrew D. A. Maidment, Predrag R. Bakić, David D. Pokrajac, Fengshan Liu, Xiquan Shi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
A novel breast image registration method is proposed to obtain a composite mammogram from several images with partial breast coverage, for the purpose of accurate breast density estimation. The breast percent density estimated as a fractional area occupied by fibroglandular tissue has been shown to be correlated with breast cancer risk. Some mammograms, however, do not cover the whole breast area, which makes the interpretation of breast density estimates ambiguous. One solution is to register and merge mammograms, yielding complete breast coverage. Due to elastic properties of breast tissue and differences in breast positioning and deformation during the acquisition of individual mammograms, the use of linear transformations does not seem appropriate for mammogram registration. Non-linear transformations are limited by the changes in the mammographic projections pixel intensity with different positions of the focal spot. We propose a novel method based upon non-linear local affine transformations. Initially, pairs of feature points are manually selected and used to compute the best fit affine transformation in their small neighborhood. Finally, Shepherd interpolation is employed to compute affine transformations for the rest of the image area. The pixel values in the composite image are assigned using bilinear interpolation. Preliminary results with clinical images show a good match of breast boundaries, providing an increased coverage of breast tissue. The proposed transformation is continued and can be controlled locally. Moreover, the method is converging to the ground truth deformation if the paired feature points are evenly distributed and its number large enough.