Shape Prior Model for Media-Adventitia Border Segmentation in IVUS Using Graph Cut - Igor Sazonov (Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, 2013)
We present a shape prior based graph cut method which does not require user initialisation. The shape prior is generalised from multiple training shapes, rather than using singular templates as priors. Weighted directed graph construction is used to impose geometrical andsmooth constraints learned from priors. The proposed cost function is built upon combining selective feature extractors. A SVM classiffier is used to determine an optimal combination of features in presence of calcification, fibrotic tissues, soft plaques, and metallic stent, each of which has its own characteristics in ultrasound images. Comparative analysis on manually labelled ground-truth shows superior performance of the proposed method compared to conventional graph cut methods.