<title>Patient site model supported change detection</title>

Abstract

This paper reports the development of a non-rigid registration technique to bring into alignment a sequence of a patient's single-view mammograms acquired at different times. This technique is applied in a patient site model supported change detection algorithm with a clinical goal of lesion detection and tracking. The algorithm flow contains four steps: preprocessing, image alignment, change detection, and site model updating. The preprocessing step includes segmentation, using standard finite normal mixture and Markov random field models, morphological processing, monotony operators, and Gaussian filtering. The site model in this research is composed of object boundaries, previous change, potential control points, and raw/segmented images. In the alignment step, the current mammogram is aligned to the site model using a two step process consisting of principle axis of the skin line followed by thin-plate spline using matched points from the potential control point pool. With the assumption of minimal global change, subtraction and thresholding will be used to create the change map that highlights significant changes. Finally, the change information will be used to update the site model. This two-step registration process facilitates change detection by aligning corresponding regions of mammograms so local change analysis can be performed in a coherent manner. The result of the change detection algorithm will be a local change and a patient specific site model showing past and present conditions.