Shape prior model for media-adventitia border segmentation in IVUS using graph cut

Authors:
Ehab Essa;Xianghua Xie;Igor Sazonov;Perumal Nithiarasu;Dave Smith
Affiliations:
Department of Computer Science, Swansea University, Swansea, UK;Department of Computer Science, Swansea University, Swansea, UK;College of Engineering, Swansea University, Swansea, UK;College of Engineering, Swansea University, Swansea, UK;ABM University NHS Trust, Swansea, UK
Venue:
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Year:
2012

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Abstract

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 and smooth constraints learned from priors. The proposed cost function is built upon combining selective feature extractors. A SVM classifier 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.