Abstract
In this paper, intravascular ultrasound (IVUS) grayscale images, acquired with a single-element mechanically rotating transducer, are processed with wavelet denoising and region-based segmentation to extract various layers of lumen contours and plaques. First, IVUS volumetric data is expanded on complex exponential multi-resolution basis functions, also known as Brushlets, which are well localized in the time and frequency domains. Brushlet denoising has previously demonstrated a great aptitude for denoising ultrasound data and removal of blood speckle. A region-based segmentation framework is then applied for detection of lumen border layers, which remains a challenging problem in IVUS image analysis for images acquired with a single element, mechanically rotating 45 MHz transducer. We evaluated a hard thresholding operator for Brushlet denoising, and compared segmentation results to manually traced lumen borders. We observed good agreement and suggest that the proposed algorithm has a potential to be used as a reliable pre-processing step for accurate lumen border detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
American Heart Association, Heart Disease and Stroke Statistics – 2006 update, American Heart Association (2006)
Nair, A., Kuban, B.D., Obuchowski, N., Vince, D.G.: Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. Ultrasound in Med. & Biol. 27(10), 1319–1331 (2001)
Katouzian, A., Baseri, B., Konofagou, E.E., Laine, A.F.: An alternative Approach to Spectrum-Based Atherosclerotic Plaque Characterization Techniques Using Intravascular Ultrasound (IVUS) Backscattered Signals. In: MICCAI-CVII 2008 (2008)
Cardinal, M.H.R., Meunier, J., Soulez, G., Maurice, R.L., Therasse, E., Cloutier, G.: Intravascular Ultrasound Image Segmentation: A Three-Dimensional Fast-Marching Method Based on Gray Level Distribution. IEEE Tran. Med. Imag. 25(5), 590–601 (2006)
Sonka, M., Zhang, X., Siebes, M., Bissing, M.S., DeJong, S.C., Collins, S.M., Mckay, C.R.: Segmentation of Intravascular Ultrasound Images: A Knowledge-Based Approach. IEEE Tran. Med. Imag. 14(4), 719–732 (1995)
Unal, G.B., Bucher, S., Carlier, S., Slabaugh, G.G., Fang, T., Tanaka, K.: Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images. IEEE Tran. Info. Tech. Biomed. 12(3), 335–347 (2008)
Klingensmith, J.D., Shekhar, R., Vince, D.G.: Evaluation of Three-Dimensional Segmentation Algorithms for the Identification of Luminal and Medial-Adventitia Borders in Intravascular Ultrasound Images. IEEE. Tran. Med. Imag. 19(10) (2000)
Besag, J.: On the Statistical Analysis of Dirty Pictures. Journal of Royal Statistical Society, Series B 48(3), 259–302 (1986)
Meyer, F., Coifman, R.R.: Brushlets: A tool for directional image analysis and image compression. Applied and computational harmonic analysis 4, 147–187 (1997)
Ausher, P., Weiss, G., Wickerhauser, M.V.: Local sine and cosine bases of Coifman and Meyer and the construction of smooth wavelets. In: Chui, C.K. (ed.) Wavelets- A tutorial in Theory and Application. Wavelet Analysis and its Applications, vol. 2, pp. 237–256. Academic Press, San Diego (1992)
Mumford, D., Shah, J.: Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems. Communication on Pure and Applied Math. 42, 544–685
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katouzian, A., Angelini, E., Lorsakul, A., Sturm, B., Laine, A.F. (2009). Lumen Border Detection of Intravascular Ultrasound via Denoising of Directional Wavelet Representations. In: Ayache, N., Delingette, H., Sermesant, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2009. Lecture Notes in Computer Science, vol 5528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01932-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-01932-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01931-9
Online ISBN: 978-3-642-01932-6
eBook Packages: Computer ScienceComputer Science (R0)