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.
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© 2009 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/978-3-642-01932-6_12
Publisher Name: Springer, Berlin, Heidelberg
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