Computerized Medical Imaging and Graphics
Volume 36, Issue 1 , Pages 11-24 , January 2012

Quantification of coronary arterial stenoses in CTA using fuzzy distance transform

  • Yan Xu

      Affiliations

    • Department of Electrical & Computer Engineering, University of Iowa, Iowa, IA 52240, United States
    • Department of Radiology, University of Iowa, Iowa, IA 52240, United States
    • School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
    • ,
  • Guoyuan Liang

      Affiliations

    • Department of Electrical & Computer Engineering, University of Iowa, Iowa, IA 52240, United States
    • Department of Radiology, University of Iowa, Iowa, IA 52240, United States
    • ,
  • Guangshu Hu

      Affiliations

    • Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
    • ,
  • Yan Yang

      Affiliations

    • Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States
    • ,
  • Jinzhao Geng

      Affiliations

    • The No. 1 Hospital, Tsinghua University, Beijing 100084, China
    • ,
  • Punam K. Saha

      Affiliations

    • Department of Electrical & Computer Engineering, University of Iowa, Iowa, IA 52240, United States
    • Department of Radiology, University of Iowa, Iowa, IA 52240, United States
    • Corresponding Author InformationCorresponding author at: Department of Electrical & Computer Engineering, University of Iowa, Iowa, IA 52240, United States. Tel.: +1 319 335 6420; fax: +1 319 335 6028.

Received 19 March 2010 ,Revised 15 March 2011 ,Accepted 24 March 2011.

References 

  1. Xu Y, Hu GS, Geng JZ, Shang LH. Automated coronary arterial tree extraction using parallel and topology. Chin J Biomed Eng. 2007;26:24–29
  2. Fayad ZA, Fuster V, Nikolaou K, Becker C. Computed tomography and magnetic resonance imaging for noninvasive coronary angiography and plaque imaging: current and potential future concepts. Circulation. 2002;106:2026–2034
  3. Lesser JR, Knickelbine T, Chavez I, Lindberg J, Schwartz RS. Ruptured plaque visualization by noninvasive coronary computed tomography angiography. Circulation. 2004;110:e311–e312
  4. Hoffmann MH, Shi H, Schmitz BL, Schmid FT, Lieberknecht M, Schulze R, et al. Noninvasive coronary angiography with multislice computed tomography. JAMA. 2005;293:2471–2478
  5. Tanaka N, Ehara M, Surmely JF, Matsubara T, Terashima M, Tsuchikane E, et al. Sixty-four-multislice computed tomography image of a ruptured coronary plaque. Circulation. 2006;114:e519–e520
  6. Cho ZH, Jones JP, Singh M. Foundation of medical imaging. New York: Wiley; 1993;
  7. Cordeiro MA, Lardo AC, Brito MS, Rosario Neto MA, Siqueira MH, Parga JR, et al. CT angiography in highly calcified arteries: 2D manual vs. modified automated 3D approach to identify coronary stenoses. Int J Cardiovasc Imaging. 2006;22:507–516
  8. Sun J, Zhang Z, Lu B, Yu W, Yang Y, Zhou Y, et al. Identification and quantification of coronary atherosclerotic plaques: a comparison of 64-MDCT and intravascular ultrasound. Am J Roentgenol. 2008;190:748–754
  9. Hur J, Kim YJ, Lee HJ, Nam JE, Choe KO, Seo JS, et al. Quantification and characterization of obstructive coronary plaques using 64-slice computed tomography: a comparison with intravascular ultrasound. J Comput Assist Tomogr. 2009;33:186–192
  10. Blackmon KN, Streck J, Thilo C, Bastarrika G, Costello P, Joseph Schoepf U. Reproducibility of automated noncalcified coronary artery plaque burden assessment at coronary CT angiography. J Thorac Imaging. 2009;24:96–102
  11. Bekkers E, Roos J. Coronary CTA: stenosis classification and quantification, including automated measures. J Cardiovasc Comput Tomogr. 2009;3(Suppl. 2):S109–S115
  12. Riedel CH, Chuah SC, Zamir M, Ritman EL. Accurate segmentation for quantitative analysis of vascular trees in 3D micro-CT images. In: Proceedings of SPIE: medical imaging. San Diego, CA. 2002;p. 256–265
  13. Florin C, Paragios N, Williams J. Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005;8:246–253
  14. Szymczak A, Stillman A, Tannenbaum A, Mischaikow K. Coronary vessel trees from 3D imagery: a topological approach. Med Image Anal. 2006;10:548–559
  15. Yang Y, Tannenbaum A, Giddens D, Wallace H. Knowledge-based 3D segmentation and reconstruction of coronary arteries using CT images. In: Presented at the 26th annual international conference of the IEEE engineering in medicine and biology society. 2004;
  16. Yang Y, Zhu L, Haker S, Tannenbaum AR, Giddens DP. Harmonic skeleton guided evaluation of stenoses in human coronary arteries. In: Presented at the int conf med image comput comput assist interv. 2005;
  17. Yang Y, Tannenbaum A, Giddens D, Stillman A. Automatic segmentation of coronary arteries using Bayesian driven implicit surfaces. In: Proceedings of 4th IEEE international symposium on biomedical imaging. 2007;p. 189–192
  18. Wang C, Frimmel H, Persson A, Smedby Ö. An interactive software module for visualizing coronary arteries in CT angiography. Int J Comput Assist Radiol Surg. 2008;3:11–18
  19. Antiga L, Ene-Iordache B, Caverni L, Cornalba GP, Remuzzi A. Geometric reconstruction for computational mesh generation of arterial bifurcations from CT angiography. Comput Med Imaging Graph. 2002;26:227–235
  20. Antiga L, Ene-Iordache B, Remuzzi A. Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography. IEEE Trans Med Imaging. 2003;22:674–684
  21. Chen Z, Molloi S. Automatic 3D vascular tree construction in CT angiography. Comput Med Imaging Graph. 2003;27:469–479
  22. Boskamp T, Rinck D, Link F, Kummerlen B, Stamm G, Mildenberger P. New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. Radiographics. 2004;24:287–297
  23. Rosenfeld A. Fuzzy digital topology. Inf Control. 1979;40:76–87
  24. Rosenfeld A. The fuzzy geometry of image subsets. Pattern Recognit Lett. 1991;2:311–317
  25. Udupa JK, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process. 1996;58:246–261
  26. Saha PK, Udupa JK. Scale-based fuzzy connectivity: a novel image segmentation methodology and its validation. In: Proceedings of SPIE: medical imaging. San Diego, CA. 1999;p. 246–257
  27. Saha PK, Udupa JK. Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation. Comput Vis Image Underst. 2001;82:42–56
  28. Lesage D, Angelini ED, Bloch I, Funka-Lea G. A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal. 2009;13:819–845
  29. Saha PK, Wehrli FW, Gomberg BR. Fuzzy distance transform: theory, algorithms, and applications. Comput Vis Image Underst. 2002;86:171–190
  30. Rosenfeld A, Pfaltz J. Distance functions in digital pictures. Pattern Recognit. 1968;1:33–61
  31. Daniensson PE. Euclidean distance mapping. Comput Graph Image Process. 1980;14:227–248
  32. Okabe N, Toriwaki J, Fukumura T. Paths and distance functions in three dimensional digitized pictures. Pattern Recognit Lett. 1983;1:205–212
  33. Borgefors G. Distance transform in arbitrary dimensions. Comput Vis Graph Image Process. 1984;27:321–345
  34. Borgefors G. Distance transformations in digital images. Comput Vis Graph Image Process. 1986;34:344–371
  35. Borgefors G. On digital distance transformation in three dimensions. Comput Vis Graph Image Process. 1996;64:368–376
  36. Klette R, Rosenfeld A. Digital geometry: geometric methods for digital picture analysis. San Francisco, CA: Morgan Kaufmann; 2004;
  37. Verwer BJH. Local distances for distance transformations in two and three dimensions. Pattern Recognit Lett. 1991;12:671–682
  38. Beckers ALD, Smeulders AWM. Optimization of length measurements for isotropic distance transformations in three dimension. CVGIP Image Underst. 1991;55:296–306
  39. Grevera GJ, Udupa JK. Shape-based interpolation of multidimensional grey-level images. IEEE Trans Med Imaging. 1996;15:881–892
  40. Saha PK, Wehrli FW. Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform. IEEE Trans Med Imaging. 2004;23:53–62
  41. Svensson S. Centres of maximal balls extracted from a fuzzy distance transform. In: Proceedings of 8th international symposium on mathematical morphology. Rio de Janeiro, Brazil. 2007;p. 19–20
  42. Svensson S. A decomposition scheme for 3D fuzzy objects based on fuzzy distance information. Pattern Recognit Lett. 2007;28:224–232
  43. Svensson S. Aspects on the reverse fuzzy distance transform. Pattern Recognit Lett. 2008;29:888–896
  44. Darabi A. Assessment of the porous media structure based on fuzzy distance transform and its map ridge. Magn Reson Imaging. 2007;25:556–1556
  45. Wehrli FW, Ladinsky GA, Jones C, Benito M, Magland J, Vasilic B, et al. In vivo magnetic resonance detects rapid remodeling changes in the topology of the trabecular bone network after menopause and the protective effect of estradiol. J Bone Miner Res. 2008;23:730–740
  46. Cachia A, Mangin J-F, Rivière D, Papadopoulos-Orfanos D, Kherif F, Bloch I, et al. A generic framework for the parcellation of the cortical surface into gyri using geodesic voronoï diagrams. Med Image Anal. 2003;7:403–416
  47. Chang G, Pakin SK, Schweitzer ME, Saha PK, Regatte RR. Adaptations in trabecular bone microarchitecture in Olympic athletes determined by 7T MRI. J Magn Reson Imaging. 2008;27:1089–1095
  48. Schaap M, Metz CT, van Walsum T, van der Giessen AG, Weustink AC, Mollet NR, et al. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal. 2009;13:701–714
  49. Saha PK, Rosenfeld A. Determining simplicity and computing topological change in strongly normal partial tilings of R2 or R3. Pattern Recognit. 2000;33:105–118
  50. Saha PK, Chaudhuri BB. 3D digital topology under binary transformation with applications. Comput Vis Image Underst. 1996;63:418–429
  51. Saha PK, Chaudhuri BB, Majumder DD. A new shape preserving parallel thinning algorithm for 3D digital images. Pattern Recognit. 1997;30:1939–1955
  52. Saha PK, Chaudhuri BB, Chanda B, Majumder DD. Topology preservation in 3D digital space. Pattern Recognit. 1994;27:295–300
  53. Saha PK, Chaudhuri BB. Detection of 3D simple points for topology preserving transformation with application to thinning. IEEE Trans Pattern Anal Mach Intell. 1994;16:1028–1032
  54. Xu Y, Saha PK, Hu G, Liang G, Yang Y, Geng J. Quantification of stenosis in coronary artery via CTA using fuzzy distance transform. In: Presented at the SPIE: medical imaging. Orlando, FL. 2009;
  55. Dodge JT, Brown BG, Bolson EL, Dodge HT. Lumen diameter of normal human coronary arteries. Influence of age, sex, anatomic variation, and left ventricular hypertrophy or dilation. Circulation. 1992;86:232–246
  56. Bühler K, Felkel P, Cruz AL. Geometric methods for vessel visualization and quantification – a survey. In:  Brunnett BHG,  Müller H editor. Geometric modelling for scientific visualization. Springer; 2003;p. 399–421
  57. Leung WH, Stadius ML, Alderman EL. Determinants of normal coronary artery dimensions in humans. Circulation. 1991;84:2294–2306
  58. Chalopin C, Finet G, Magnin IE. Modeling the 3D coronary tree for labeling purposes. Med Image Anal. 2001;5:301–315
  59. Xue P, Wilson DL. Detection of moving objects in pulsed-X-ray fluoroscopy. J Opt Soc Am A. 1998;15:375–388
  60. Cohen J. Weighed kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull. 1968;70:213–220
  61. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46

PII: S0895-6111(11)00060-7

doi: 10.1016/j.compmedimag.2011.03.004

Computerized Medical Imaging and Graphics
Volume 36, Issue 1 , Pages 11-24 , January 2012

Article Tools

* Image Usage & Resolution