Abstract
Background
Intravascular ultrasound (IVUS) provides axial greyscale images, allowing the assessment of the vessel wall and the surrounding tissues. Several studies have described automatic segmentation of the luminal boundary and the media–adventitia interface by means of different image features.
Purpose
The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image features for segmenting IVUS images. The study is focused on Volcano 20 MHz frames not containing plaque or containing fibrotic plaques, and, in principle, it could not be applied to frames containing shadows, calcified plaques, bifurcations and side vessels.
Methods
Several image filters, textural descriptors, edge detectors, noise and spatial measures were taken into account. The assessment is based on classification techniques previously used for IVUS segmentation, assigning to each pixel a continuous likelihood value obtained using support vector machines (SVMs). To retrieve relevant features, sequential feature selection was performed guided by the area under the precision–recall curve (AUC-PR).
Results
Subsets of relevant image features for lumen, plaque and surrounding tissues characterization were obtained, and SVMs trained with these features were able to accurately identify those regions. The experimental results were evaluated with respect to ground truth segmentations from a publicly available dataset, reaching values of AUC-PR up to 0.97 and Jaccard index close to 0.85.
Conclusion
Noise-reduction filters and Haralick’s textural features denoted their relevance to identify lumen and background. Laws’ textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process.
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References
Aja-Fernández S, Alberola-Lopez C (2006) On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans Image Process 15(9):2694–2701
Alberti M, Balocco S, Gatta C, Ciompi F, Pujol O, Silva J, Carrillo X, Radeva P (2012) Automatic bifurcation detection in coronary IVUS sequences. IEEE Trans Biomed Eng 59(4):1022–1031
Alpaydin E (2010) Introduction to machine learning, 2nd edn. MIT Press, Cambridge
Balocco S, Gatta C, Ciompi F, Wahle A, Radeva P, Carlier S, Unal G, Sanidas E, Mauri J, Carillo X, Kovarnik T, Wang CW, Chen HC, Exarchos TP, Fotiadis DI, Destrempes F, Cloutier G, Pujol O, Alberti M, Mendizabal-Ruiz EG, Rivera M, Aksoy T, Downe RW, Kakadiaris IA (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38(2):70–90
Bovik A, Clark M, Geisler W (1990) Multichannel texture analysis using localized spatial filters. Pattern Anal Mach Intell IEEE Trans 12(1):55–73
Caballero K, Barajas J, Pujol O, Rodriguez O, Radeva P (2007) Using reconstructed IVUS images for coronary plaque classification. In: Engineering in Medicine and Biology Society, EMBS 2007. 29th annual international conference of the IEEE, pp 2167–2170
Ciompi F (2008) Ecoc-based plaque classification using in-vivo and ex-vivo intravascular ultrasound data. Master’s thesis, CVC-UAB
Ciompi F, Pujol O, Gatta C, Alberti M, S B, Carrillo X, Mauri-Ferre J, Radeva P (2012) Holimab: a holistic approach for mediaadventitia border detection in intravascular ultrasound. Med Image Anal 16:1085–1100
Crimmins TR (1985) Geometric filter for speckle reduction. Appl Opt 24(10):1438–1443
Giannoglou V, Stavrakoudis D, Theocharis J (2012) IVUS-based characterization of atherosclerotic plaques using feature selection and svm classification. In: 2012 IEEE 12th international conference on bioinformatics bioengineering (BIBE), pp 715–720
Giannoglou VG, Stavrakoudis DG, Theocharis JB, Petridis V (2015) Genetic fuzzy rule based classification systems for coronary plaque characterization based on intravascular ultrasound images. Eng Appl Artif Intell 38:203–220
Gil D, Hernandez A, Rodriguez O, Mauri J, Radeva P (2006) Statistical strategy for anisotropic adventitia modelling in IVUS. IEEE Trans Med Imaging 25(6):768–778
Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. Syst Man Cybern IEEE Trans SMC 3(6):610–621
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin
Jourdain M, Meunier J, Sequeira J, Cloutier G, Tardif JC (2010) Intravascular ultrasound image segmentation: a helical active contour method. In: Image processing theory tools and applications (IPTA), 2010 2nd international conference on, pp 92–97
Katouzian A, Angelini E, Carlier S, Suri J, Navab N, Laine A (2012) A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images. IEEE Trans Inf Technol Biomed 16(5):823–834
Koga T, Uchino E, Suetake N (2011) Automated boundary extraction and visualization system for coronary plaque in IVUS image by using fuzzy inference-based method. In: 2011 IEEE international conference on fuzzy systems (FUZZ), pp 1966–1973
Liu Y, Shriberg E (2007) Comparing evaluation metrics for sentence boundary detection. In: Acoustics, speech and signal processing, ICASSP 2007. IEEE international conference on, vol 4, pp IV-185–IV-188
Loizou C, Pattichis C (2008) Despeckle filtering algorithms and software for ultrasound imaging. Morgan and Claypool, San Rafael
Mendizabal-Ruiz EG, Rivera M, Kakadiaris IA (2013) Segmentation of the luminal border in intravascular ultrasound b-mode images using a probabilistic approach. Med Image Anal 17(6):649–670
Moreland K (2009) Diverging color maps for scientific visualization. In: Bebis G, Boyle R, Parvin B, Koracin D, Kuno Y, Wang J, Pajarola R, Lindstrom P, Hinkenjann A, Encarnao ML, Silva CT, Coming D (eds) Advances in visual computing. Lecture notes in computer science, vol 5876. Springer, Berlin, pp 92–103
Nissen SE, Yock P (2001) Intravascular ultrasound: novel pathophysiological insights and current clinical applications. Circulation 103(4):604–616
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):971–987
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Pujol O, Rosales M, Radeva P, Nofrerias-Fernández E (2003) Intravascular ultrasound images vessel characterization using adaboost. In: Magnin I, Montagnat J, Clarysse P, Nenonen J, Katila T (eds) Functional imaging and modeling of the heart. Lecture notes in computer science, vol 2674. Springer, Berlin, pp 242–251
Sanz-Requena R, Moratal D, García-Sánchez DR, Bodí V, Rieta JJ, Sanchis JM (2007) Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies. Comput Med Imaging Graph 31(2):71–80
Shalev-Shwartz S, Zhang T (2013) Stochastic dual coordinate ascent methods for regularized loss. J Mach Learn Res 14(1):567–599
Shapiro R, Haralick R (1992) Computer and robot vision. Addison-Wesley, Boston
Taki A, Najafi Z, Roodaki A, Setarehdan S, Zoroofi R, Konig A, Navab N (2008) Automatic segmentation of calcified plaques and vessel borders in IVUS images. Int J Comput Assist Radiol Surg 3(3–4):347–354
Unal G, Bucher S, Carlier S, Slabaugh G, Fang T, Tanaka K (2008) Shape-driven segmentation of the arterial wall in intravascular ultrasound images. IEEE Trans Inf Technol Biomed 12(3):335–347
Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org/
Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224
Yu Y, Acton S (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11(11):1260–1270
Zhu X, Zhang P, Shao J, Cheng Y, Zhang Y, Bai J (2011) A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51(2):181–189
Acknowledgments
The present work has been partially funded by the National Agency for Science and Technology Promotion (ANPCyT, Argentina) within the projects PICT 2010-1287 and PICT 2014-1730.
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Lo Vercio, L., Orlando, J.I., del Fresno, M. et al. Assessment of image features for vessel wall segmentation in intravascular ultrasound images. Int J CARS 11, 1397–1407 (2016). https://doi.org/10.1007/s11548-015-1345-4
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DOI: https://doi.org/10.1007/s11548-015-1345-4