Abstract
Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. In this paper, we present a method for the segmentation of the luminal border using IVUS radio frequency (RF) data. Specifically, we parameterize the lumen contour using Fourier series. This contour is deformed by minimizing a cost function that is formulated using a probabilistic approach in which the a priori term is obtained using the prediction confidence of a Support Vector Machine classifier using features extracted from the RF signal. We evaluated the performance of our method by comparing our results with manual segmentations from two expert observers on 280 frames from eight 40 MHz IVUS sequences from rabbits and pigs. The performance was evaluated using the Dice similarity coefficient, coefficient of determination, and linear regressions of the lumen area for each frame. Our results indicate the feasibility of our method for the segmentation of the lumen from IVUS RF data.
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Keywords
- Support Vector Machine Model
- Wavelet Packet
- Intravascular Ultrasound
- Manual Segmentation
- Dice Similarity Coefficient
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References
Mojsilovic, A., Popovic, M., Amodaj, N., Babic, R., Ostojic, M.: Automatic segmentation of intravascular ultrasound images: A texture-based approach. Annals of Biomedical Engineering 25(6), 1059–1071 (1997)
Haas, C., Ermert, H., Holt, S., Grewe, P., Machraoui, A., Barmeyer, J.: Segmentation of 3D intravascular ultrasonic images based on a random field model. Ultrasound in Medicine and Biology 26(2), 297–306 (2000)
Luo, Z., Wang, Y., Wang, W.: Estimating coronary artery lumen area with optimization-based contour detection. IEEE Transactions on Medical Imaging 22, 564–566 (2003)
Brusseau, E., de Korte, C.: Fully automatic luminal contour segmentation in intracoronary ultrasound imaging - a statistical approach. IEEE Transactions on Medical Imaging 23(5), 554–566 (2004)
Cardinal, M., Meunier, J., Soulez, G., Maurice, R., Therasse, E., Cloutier, G.: Intravascular ultrasound image segmentation: A three-dimensional fast-marching method based on gray level distributions. IEEE Transactions on Medical Imaging 25(5), 590–601 (2006)
dos Santos Filho, E., Yoshizawa, M., Tanaka, A., Saijo, Y.: A study on intravascular ultrasound image processing. Record of Electrical and Communication Engineering Conversazione 74(2), 30–33 (2006)
Unal, G., Bucher, S., Carlier, S., Slabaugh, G., Fang, T., Tanaka, K.: Shape-driven segmentation of the arterial wall in intravascular ultrasound images. IEEE Transactions on Information Technology in Biomedicine 12(3), 335–347 (2008)
Papadogiorgaki, M., Mezaris, V., Chatzizisis, Y., Giannoglou, G., Kompatsiaris, I.: Image analysis techniques for automated IVUS contour detection. Ultrasound in Medicine and Biology 34(9), 1482–1498 (2008)
Katouzian, A., Baseri, B., Konofagou, E., Laine, A.: Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures. In: Proc. SPIE Medical Imaging 2008: Ultrasonic Imaging and Signal Processing, San Diego, CA, February 16-21 (2008)
Ciompi, F., Pujol, O., Fernández-Nofrerías, E., Mauri, J., Radeva, P.: ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 869–876. Springer, Heidelberg (2009)
Cardinal, M., Soulez, G., Tardif, J., Meunier, J., Cloutier, G.: Fast-marching segmentation of three-dimensional intravascular ultrasound images: A pre-and post-intervention study. Medical Physics 37(7), 3633–3647 (2010)
Zhu, X., Zhang, P., Shao, J., Cheng, Y., Zhang, Y., Bai, J.: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51(2), 181–189 (2011)
Moraes, M., Furuie, S.: Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction. Ultrasound in Medicine and Biology 37, 1486–1499 (2011)
Hiro, T., Leung, C., Russo, R., Karimi, H., Farvid, A., Tobis, J.: Variability of a three-layered appearance in intravascular ultrasound coronary images: A comparison of morphometric measurements with four intravascular ultrasound systems. American Journal of Cardiac Imaging 10(4), 219–227 (1996)
Mintz, G., Nissen, S., Anderson, W., Bailey, S., Erbel, R., Fitzgerald, P., Pinto, F., Rosenfield, K., Siegel, R., Tuzcu, E., Yock, P.: American College of Cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (IVUS). Journal of the American College of Cardiology 37(5), 1478–1492 (2001)
Nair, A., Margolis, M., Kuban, B., Vince, D.: Automated coronary plaque characterisation with intravascular ultrasound backscatter: Ex vivo validation. Eurointervention 3(1), 113–120 (2007)
Mendizabal-Ruiz, E., Biros, G., Kakadiaris, I.A.: Towards Extra-Luminal Blood Detection from Intravascular Ultrasound Radio Frequency Data. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 396–403. Springer, Heidelberg (2011)
Mendizabal-Ruiz, E., Rivera, M., Kakadiaris, I.: A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, AK, June 24-26, pp. 1–8 (2008)
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Mendizabal-Ruiz, E.G., Kakadiaris, I.A. (2012). Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_56
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DOI: https://doi.org/10.1007/978-3-642-33418-4_56
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