Pranav Bhagirath

72 Chapter 3 15. Karimaghaloo Z et al, 2012. Automatic detection of gadolinium-enhancingmultiple sclerosis lesions in brain mri using conditional random fields. IEEE Trans. Med. Imaging 31 (6), 1181– 1194. 16. Kim RJ et al, 1999. Relationship of mri delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100 (19), 1992–2002. 17. Kirisli H et al 2013. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 8 (17), 856–876. 18. Knowles B et al, 2010. 3-D visualization of acute RF ablation lesions using MRI for the simultaneous determination of the patterns of necrosis and edema. IEEE Trans. Biomed. Eng. 57 (6), 1467–1475. 19. Kolipaka A et al, 2005. Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images. Int. J. Cardiovasc. Imaging 21 (2-3), 303–311. 20. Lafferty J et al, 2001. Conditional random fields: probabilistic models for segmenting and labelling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, pp. 282–289. 21. Lu Y et al, 2012. Automated quantification of myocardial infarction using graph cuts on contrast delayed enhanced magnetic resonance images. Quant. Imaging Med. Surg. 2 (2), 81. 22. Oakes R et al, 2009. Detection and quantification of left atrial structural remodelling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 119 (13), 1758–1767. 23. Otsu N et al, 1975. A threshold selection method from gray-level histograms. Automatica 11 (285-296), 23–27. 24. Petitjean C et al, 2015. Right ventricle segmentation from cardiac mri: a collation study. Med. Image Anal. 19 (1), 187–202 .http://dx.doi.org/10.1016/j.media.2014.10.004. 25. Pop M et al, 2013. Quantification of fibrosis in infarcted swine hearts by ex vivo late gadolinium-enhancement and diffusion-weighted mri methods. Phys. Med. Biol. 58 (15), 5009. 26. Positano V et al, 2005. A fast and effective method to assess myocardial necrosis by means of contrast magnetic resonance imaging. J. Cardiovasc. Magn. Reson. 7 (2), 487–494. 27. Rajchl M et al, 2014. Comparison of semi-automated scar quantification techniques using high- resolution, 3-dimensional late-gadolinium-enhancement magnetic resonance imaging. Int. J. Cardiovasc. Imaging 1–9. 28. Ravanelli D et al, 2014. A novel skeleton based quantification and 3d volumetric visualization of left atrium fibrosis using late gadolinium enhancement magnetic resonance imaging. IEEE Trans. Med. Imaging 33 (2).