3D-3D Tubular Organ Registration and Bifurcation Detection from CT Images
Jinghao Zhou, Sukmoon Chang, Dimitris Metaxas, G. S. Mageras · InTech eBooks · 2010
In this chapter, we present a novel method for tubular organs registration based on the automatically detected bifurcation points of the tubular organs. We first perform a 3D tubular organ segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respirationcorrelated CT images. Our method has over 96% success rate for detecting bifurcation points. We present bery promising results of our method applied to the registration of the planning and respiration-correlated CT images. On average, the mean distance and the rootmean-square error (RMSE) of the corresponding bifurcation points between the respirationcorrelated images and the registered planning images are less than 2.7 mm.