New Approaches for Plaque Component Analysis in Intravascular Ultrasound (IVUS) Images

  • Arash Taki
  • Alireza Roodaki
  • Sara Avansari
  • Ali Bigdelou
  • Amin Katouzian
  • Nassir Navab
Chapter

Abstract

Heart attack and stroke are the major causes of human death and atherosclerotic plaques are the most common effect of cardiovascular disease. Intravascular ultrasound (IVUS), a diagnostic imaging technique, offers a unique view of the morphology of the arterial plaque and displays the morphological and histological properties of a cross section of the vessel. Limitations of the gray-scale IVUS manual plaque assessment have led to the development of quantitative techniques for analysis of characteristics of plaque components.

In vivo plaque characterization with the so-called Virtual Histology (VH)-IVUS, which is based on the ultrasound RF signal processing, is widely available for atherosclerosis plaque characterization in IVUS images. However, it suffers from a poor longitudinal resolution due to the ECG-gated acquisition. The focus of this chapter is to provide effective methods for image-based vessel plaque characterization via IVUS image analysis to overcome the limitations of current techniques. The proposed algorithms are also applicable to the large amount of the IVUS image sequences obtained from patients in the past, where there is no access to the corresponding radiofrequency (RF) data. Since the proposed method is applicable to all IVUS frames of the heart cycle, it outperforms the longitudinal resolution of the so-called VH method.

The procedures of analyzing gray-scale IVUS images can be divided into two separated aspects: (1) detecting the vessel borders to extract the region called “plaque area” and (2) characterizing the atherosclerosis plaque composition. The latter one consists of two main steps: in the first one, known as feature extraction, the plaque area of the cross-sectional IVUS image is modeled using appropriate features.

The second step based on learning techniques assists the classifier in distinguishing different classes more precisely and in assigning labels to each of the samples generated by feature extraction within the first step.

In vivo and ex vivo validation procedures were used, where the results proved the efficiency of the proposed algorithms for vessel plaque characterization via IVUS images. A graphic user interface (GUI) is designed as an effective image processing tool which enables cardiologists with a complete IVUS image processing tool from border detection to plaque characterization. The algorithms developed within this thesis lead to the enhancement of the longitudinal resolution of plaque composition analysis.

Keywords

Entropy Formalin Catheter Cage Sodium Chloride 

References

  1. 1.
    Theodoridis S, Koutroumbas K (2006) Pattern recognition, 5th edn. ElsevierGoogle Scholar
  2. 2.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  3. 3.
    Sertel O, Kong J, Shimada H, Catalyurek U, Saltz J, Gurcan M (2008) Computeraided prognosis of neuroblastoma on whole-slide images: classification of stromal development. SPIE Medical ImagingGoogle Scholar
  4. 4.
    Gonzalo N, Garcia-Garcia HM, Ligthart J, Rodriguez-Granillo G, Meliga E, Onuma Y, Schuurbiers JCH, Bruining N, Serruys PW (2008) Coronary plaque composition as assessed by grayscale ultrasound and radiofrequency spectral data analysis. Int J Cardiovasc Imaging 24:811–818PubMedCrossRefGoogle Scholar
  5. 5.
    Sorensen L, Shaker SB, de Bruijne M (2008) Texture classification in lung CT using local binary patterns. Med Image Comput Comput Assist Interv 11:934–941PubMedGoogle Scholar
  6. 6.
    Galloway MM (1975) Texture analysis using gray level run lengths. J Comput Graph Image Process 4:172–179CrossRefGoogle Scholar
  7. 7.
    Welling M (2007) Fisher linear discriminant analysis. Class notes. Department of Computer Science, University of Toronto, Toronto, ONGoogle Scholar
  8. 8.
    Bishop CM (2006) Pattern recognition and matchine learning, 1st edn. SpringerGoogle Scholar
  9. 9.
    Chang C, Lin C (2001) Libsvm: a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm
  10. 10.
    Fan RE, Chen PH, Lin CJ (2005) Working set selection using the second order information for training support vector machine. Mach Learn Res 6:1889–1918Google Scholar
  11. 11.
    Nair A, Margolis MP, Kuban BD, Vince DG (2007) Automated coronary plaque characterization with intravascular ultrasound backscatter: ex-vivo validation. EuroIntervention 3:113–120PubMedGoogle Scholar
  12. 12.
    Vince DG, Dixon KJ, Cothren RM, Cornhill JF (2000) Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput Med Imaging Graph 24:221–229PubMedCrossRefGoogle Scholar
  13. 13.
    Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefGoogle Scholar
  14. 14.
    Sales FJR, Falcao JLAA, Falcao BAA, Lemos PA, Furuie SS (2008) Evidences of possible necrotic-core artifact around dense calcium in virtual histology images. Comput Cardiol 35:545–548Google Scholar
  15. 15.
    Nason GP, Silverman BW (1995) The stationary wavelet transform and some statistical applications. Lect Notes Stat 35:281–300CrossRefGoogle Scholar
  16. 16.
    Coifman RR, Wickerhauser MV (1992) Entropy based algorithm for best basis selection. IEEE Trans Inf Theory 38(2):713–718CrossRefGoogle Scholar
  17. 17.
    Rodriguez-Granillo GA, McFadden EP, Valgimigli M, Van Mieghem CA, Regar E, De Feyter PJ, Serruys PW (2006) Coronary plaque composition of nonculprit lesions, assessed by in vivo intracoronary ultrasound radio frequency data analysis, is related to clinical presentation. Am Heart J 151(5):1020–1024PubMedCrossRefGoogle Scholar
  18. 18.
    Mintz GS, Nissen SE, Anderson WD, Bailey SR, Erbel R, Fitzgerald PJ, Pinto FJ, Rosenfield K, Siegel RJ, Tuzcu EM, Yock PG (2001) Clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (ivus). J Am Coll Cardiol 37:1479–1491CrossRefGoogle Scholar
  19. 19.
    Bruining N, Verheye S, Knaapen M, Somers P, Roelandt JRTC, Regar E, Heller I, De Winter S, Ligthart J, Van Langenhove G, De Feijter PJ, Serruys PW, Hamers R (2007) Three-dimensional and quantitative analysis of atherosclerotic plaque composition by automated differential echogenicity. Catheter Cardiovasc Interv 70(7):968–978PubMedCrossRefGoogle Scholar
  20. 20.
    König A, Margolis MP, Virmani R, Holmes D, Klauss V (2008) Technology insight: in vivo coronary plaque classification by intravascular ultrasonography radiofrequency analysis. Cardiovasc Med 5(4):219–229Google Scholar
  21. 21.
    Haralick R, Shanmugam K, Dinstein I (1973) Textural features of image classification. IEEE Trans Syst Man Cybern 6:610–621CrossRefGoogle Scholar
  22. 22.
    Tang X (1998) Texture information in run length matrices. IEEE Trans Image Process 7(11):1602–1609PubMedCrossRefGoogle Scholar
  23. 23.
    Gil D, Radeva P, Saludes J, Mauri J (2000) Segmentation of artery wall in coronary IVUS images: a probabilistic approach. Comput Cardiol: 687–690Google Scholar
  24. 24.
    Nasu K, Tsuchikane E, Katoh O, Vince DG, Virmani R, Surmely JF, Murata A, Takeda Y, Ito T, Ehara M, Matsubara T, Terashima M, Suzuki T (2006) Accuracy of in vivo coronary plaque morphology assessment: a validation study of in vivo virtual histology compared with in vitro histopathology. J Am Coll Cardiol 47(12):2405–2412PubMedCrossRefGoogle Scholar
  25. 25.
    Virmani R, Burke AP, Farb A, Kolodgie FD (2006) Pathology of the vulnerable plaque. J Am Coll Cardiol 47(8):c13–c18PubMedCrossRefGoogle Scholar
  26. 26.
    Sales FJR, Falcao JLAA, Falcao BAA, Furuie SS, Lemos PA (2009) Estimation of coronary atherosclerotic plaque composition based only on gray scale intravascular ultrasound images. Comput CardiolGoogle Scholar
  27. 27.
    Escalera S, Pujol O, Mauri J, Radeva P (2009) Intravascular ultrasound tissue characterization with sub-class error-correcting output codes. J Signal Process Syst 55(1–3):35–47CrossRefGoogle Scholar
  28. 28.
    Rieber J, Meissner O, Babaryka G, Reim S, Oswald M, Koenig A, Schiele TM, Shapiro M, Theisen K, Reiser MF, Klauss V, Hoffmann UGoogle Scholar
  29. 29.
    Katouzian A, Baseri B, Konofagou EE, Laine AF (2008) An alternative approach to spectrum-based atherosclerotic plaque characterization techniques using intravascular ultrasound (IVUS) backscattered signals. 2nd Workshop on computer vision for intravascular and intracardiac imaging, MICCAIGoogle Scholar
  30. 30.
    Katouzian A, Sathyanarayana S, Baseri B, Konofagou EE, Carlier G (2008) Challenges in atherosclerotic plaque characterization with intravascular ultrasound (ivus): from data collection to classification. IEEE Trans Inf Technol Biomed 12(3): 315–327PubMedCrossRefGoogle Scholar
  31. 31.
    Topol EJ (2007) Textbook of cardiovascular medicine, 3rd edn. Lippincott Williams & Wilkins, Philadelphia, PAGoogle Scholar
  32. 32.
    Virmani R, Kolodgie FD, Burke AP, Farb A, Schwartz SM (2000) Lessons from sudden coronary death a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 20:1262–1275PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • Arash Taki
    • 1
  • Alireza Roodaki
    • 2
  • Sara Avansari
    • 3
  • Ali Bigdelou
    • 1
  • Amin Katouzian
    • 1
  • Nassir Navab
    • 1
  1. 1.Informatic DepartmentTechnical University of MunichGarching b. MunichGermany
  2. 2.Department SSESUPELECGif-sur-YvetteFrance
  3. 3.Department of Computer ScienceUniversity of TehranTehranIran

Personalised recommendations