Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images

PLoS ONE

Volumn 9, Issue 11, 2014, Pages

  Gao, Zhifan ^a,b,c   Guo, Wei ^d   Liu, Xin ^b,c   Huang, Wenhua ^e   Zhang, Heye ^b,c   Tan, Ning ^d   Hau, William Kongto ^f   Zhang, Yuan Ting ^b,c,g   Liu, Huafeng ^h  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^d GUANGDONG PROVINCIAL PEOPLE'S HOSPITAL   (China)

^e SOUTHERN MEDICAL UNIVERSITY   (China)

^f UNIVERSITY OF HONG KONG   (Hong Kong)

^g CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^h ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ATHEROSCLEROTIC PLAQUE; CLINICAL ARTICLE; COMPUTER AIDED DESIGN; COMPUTER MODEL; CONTROLLED STUDY; CORRELATION ANALYSIS; HUMAN; IN VIVO STUDY; INTRAVASCULAR ULTRASOUND; INTRAVASCULAR ULTRASOUND SYSTEM; MARKOV RANDOM FIELD; RAYLEIGH MIXTURE MODEL; SENSITIVITY AND SPECIFICITY; ALGORITHM; CALCINOSIS; COMPUTER ASSISTED DIAGNOSIS; CORONARY BLOOD VESSEL; ECHOGRAPHY; PROCEDURES;

ALGORITHMS; CALCINOSIS; CORONARY VESSELS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PLAQUE, ATHEROSCLEROTIC;

EID: 84910665621     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0109997     Document Type: Article

References (46)

1. Fustera V, Moreno PR, Fayad ZA, Corti R, Badimon JJ (2005) Atherothrom-bosis and high-risk plaque: part I: evolving concepts. Journal of the American College of Cardiology 46: 937-954.
2. Alberti M, Balocco S, Gatta C, Ciompi F, Pujol O, et al. (2012) Automatic bifurcation detection in coronary ivus sequences. IEEE Transactions on Biomedical Engineering 59: 1022-1031.
3. Schoenhagen P, De Franco A, Nissen SE, Tuczu EM (2005) IVUS Made Easy. London: Taylor & Francis.
4. Plissiti ME, Fotiadis DI, Michalis LK, Bozios GE (2004) An automated method for lumen and media-adventitia border detection in a sequence of ivus frames. IEEE Transactions on Information Technology in Biomedicine 8: 131-141.
5. Meier DS, Cothren RM, Vince DG, Cornhill JF (1997) Automated morphometry of coronary arteries with digital image analysis of intravascular ultrasound. American Heart Journal 133: 681-690.
6. Herrington DM, Johnson T, Santago P, Snyder WE (1992) Semi-automated boundary detection for intravascular ultrasound. In: Proceedings of Computers in Cardiology. Piscataway: IEEE, pp. 103-106.
7. Mojsilović A, Popović M, Amodaj N, Babić R, Ostojić M (1997) Automatic segmentation of intravascular ultrasound images: a texture-based approach. Annals of Biomedical Engineering 25: 1059-1071.
8. Papadogiorgaki M, Mezaris V, Chatzizisis YS, Giannoglou GD, Kompatsiaris I (2007) Texture analysis and radial basis function approximation for ivus image segmentation. The Open Biomedical Engineering Journal 1: 53. Available: http://www.benthamscience.com/open/tobej/openaccess2.htm.
9. Brusseau E, de Korte CL, Mastik F, Schaar J, van der Steen AFW (2004) Fully automatic luminal contour segmentation in intracoronary ultrasound imaging - a statistical approach. IEEE Transactions on Medical Imaging 23: 554-566.
10. Cardinal MH, Meunier J, Soulez G, Maurice RL, Therasse É, et al. (2006) Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions. IEEE Transactions on Medical Imaging 25: 590-601.
11. Kovalski G, Beyar R, Shofti R, Azhari H (2000) Three-dimensional automatic quantitative analysis of intravascular ultrasound images. Ultrasound in Medicine & Biology 26: 527-537.
12. Gil D, Radeva P, Mauri F (2002) IVUS segmenation via a regularized curvature flow. Congreso Anual de la Sociedad Espanola de Ingeniería Biomédica: 133-136.
13. Roodaki A, Najafi Z, Soltanzadi A, Taki A, Setarehdan SK, et al. (2009) Detection of outer layer of the vessel wall and characterization of calcified plaques in ivus images. In: Advances in Computer Science and Engineering, Berlin: Springer. pp. 629-636.
14. Friedman A (2011) Partial differential equations. N. Chelmsford: Dover Publications.
15. Greenland P, Bonow RO, Brundage BH, Budoff MJ, Eisenberg MJ, et al. (2007) ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the american college of cardiology foundation clinical expert consensus task force. Journal of the American College of Cardiology 49: 378-402.
16. O'Rourke RA, Brundage BH, Froelicher VF, Greenland P, Grundy SM, et al. (2000) American college of cardiology/american heart association expert consensus document on electron-beam computed tomography for the diagnosis and prognosis of coronary artery disease. Journal of the American College of Cardiology 36: 326-340.
17. Wayhs R, Zelinger A, Raggi P (2002) High coronary artery calcium scores pose an extremely elevated risk for hard events. Journal of the American College of Cardiology 39: 225-230.
18. Raggi P, Shaw LJ, Berman DS, Callister TQ (2004) Prognostic value of coronary artery calcium screening in subjects with and without diabetes. Journal of the American College of Cardiology 43: 1663-1669.
19. Shaw LJ, Raggi P, Callister TQ, Berman DS (2006) Prognostic value of coronary artery calcium screening in asymptomatic smokers and non-smokers. European Heart Journal 27: 968-975.
20. Shaw LJ, Raggi P, Berman DS, Callister TQ (2006) Coronary artery calcium as a measure of biologic age. Atherosclerosis 188: 112-119.
21. Dey N, Roy AB, Das P, Das A, Chaudhuri SS (2012) Detection and measurement of arc of lumen calcification from intravascular ultrasound using harris corner detection. In: National Conference on Computing and Communication Systems. Piscataway: IEEE, pp. 1-6.
22. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11: 23-27.
23. Zhang Q, Wang Y, Wang W, Ma J, Qian J, et al. (2010) Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform. Ultrasound in Medicine & Biology 36: 111-129.
24. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Hoboken: John Wiley & Sons.
25. Santos Filho E, Saijo Y, Tanaka A, Yoshizawa M (2008) Detection and quantification of calcifications in intravascular ultrasound images by automatic thresholding. Ultrasound in Medicine & Biology 34: 160-165.
26. Xing D, Yang F, Gao J, Qiu X, Tu S, et al. (2013) Intravascular ultrasound image hard plaque recognition and media-adventitia border detection. In: World Congress on Medical Physics and Biomedical Engineering. Berlin: Springer, pp. 931-934.
27. Mintz GS, Nissen SE, Anderson WD, Bailey SR, Erbel R, et al. (2001) American college of cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (IVUS) 33A report of the american college of cardiology task force on clinical expert consensus documents developed in collaboration with the european society of cardiology endorsed by the society of cardiac angiography and interventions. Journal of the American College of Cardiology 37: 1478-1492.
28. Nilsson NJ (1982) Principles of artificial intelligence. Berlin: Springer.
29. Goodman JW (2007) Speckle phenomena in optics: theory and applications. Greenwood Village: Roberts and Company Publishers.
30. Cardinal MHR, Meunier J, Soulez G, Thérasse É, Cloutier G (2003) Intravascular ultrasound image segmentation: a fast-marching method. In: Medical Image Computing and Computer-Assisted Intervention, Berlin: Springer. pp. 432-439.
31. Seabra JC, Ciompi F, Pujol O, Mauri J, Radeva P, et al. (2011) Rayleigh mixture model for plaque characterization in intravascular ultrasound. IEEE Transactions on Biomedical Engineering 58: 1314-1324.
32. Bishop CM (2006) Pattern recognition and machine learning. Berlin: Springer.
33. Li SZ (2009) Markov random field modeling in image analysis. Berlin: Springer.
34. Murphy KP (2012) Machine learning: a probabilistic perspective. Cambridge: MIT Press.
35. Yedidia JS, Freeman WT, Weiss Y (2001) Bethe free energy, kikuchi approximations, and belief propagation algorithms. Mitsubishi Electric Research laboratories Available: http://www.merl.com.
36. Yedidia JS, Freeman WT, Weiss Y (2003) Understanding belief propagation and its generalizations. In: Exploring Artificial Intelligence in the New Millennium, San Francisco: Morgan Kaufmann Publishers. pp. 239-269.
37. Mezard M, Montanari A (2009) Information, physics, and computation. Oxford: Oxford University Press.
38. Kschischang FR, Frey BJ, Loeliger HA (2001) Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47: 498-519.
39. Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. The Statistician 32: 307-317.
40. Gao Z, Hau WK, Zhang H, Zhang YT (2014) Automatic detection of calcified plaque with acoustic shadowing. In: IFMBE International Conference on Health Informatics. Berlin: Springer, pp. 197-199.
41. Ravindraiah R, Tejaswini K (2014) IVUS image segmentation by using expectation-maximization approach. International Journal of Advanced Research in Computer and Communication Engineering 3. Available: http://www.ijarcce.com/volume-3-issue-2.html.
42. Seabra J, Sanches J, Ciompi F, Radeva P (2010) Ultrasonographic plaque characterization using a rayleigh mixture model. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Piscataway: IEEE, pp. 1-4.
43. Goodman JW (1976) Some fundamental properties of speckle. Journal of the Optical Society of America 66: 1145-1150.
44. Nguyen TM, Wu Q (2012) Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42: 193-202.
45. Antoniou A, Lu WS (2007) Practical optimization: algorithms and engineering applications. Berlin: Springer.
46. Forbes C, Evans M, Hastings N, Peacock B (2011) Statistical distributions. Hoboken: John Wiley & Sons.