Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust non-rigid point registration based on feature-dependant finite mixture model
Pattern Recognition Letters
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Ultrasound elastography is a promising technique for the detection of breast cancer. Despite being proven to be a useful method in clinical studies, no studies have looked at directly correlating information in in-vivo ultrasound elastography images with histopathology images (regarded as the gold standard) using image registration. In this paper, expert knowledge from clinicians is utilised as constraints to register 2D elastography and histopathology images based on corresponding features identified by clinicians such as tumours and fibrous structures. The recently proposed coherent point drift (CPD) algorithm by Myronenko and Song [11] was applied to align the corresponding feature points and the thin-plate splines method of Bookstein [1] is then used to warp the images. The registered images were then overlaid. It was found that in elastography images, the stiffness of malignant tumours tend to extend beyond the tumour boundaries identified in the histopathology images, and there were many stiff areas indicated in the elastography images where no corresponding features could be identified in the histopathology images. The study thus provides some new insight into the relationship of elastography and histopathology as well as suggests further work is needed to better understand how to interpret patterns in elastography images.