Assessment of image features for vessel wall segmentation in intravascular ultrasound images
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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.
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.
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).
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.
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.
KeywordsIVUS Vessel wall Segmentation Feature selection SVM
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.
Compliance with ethical standards
Conflict of interest
The authors have no conflicts of interest.
- 3.Alpaydin E (2010) Introduction to machine learning, 2nd edn. MIT Press, CambridgeGoogle Scholar
- 4.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):70–90CrossRefPubMedGoogle Scholar
- 6.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 2167–2170Google Scholar
- 7.Ciompi F (2008) Ecoc-based plaque classification using in-vivo and ex-vivo intravascular ultrasound data. Master’s thesis, CVC-UABGoogle Scholar
- 8.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 16:1085–1100Google Scholar
- 10.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 715–720Google Scholar
- 15.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 92–97Google Scholar
- 17.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 1966–1973Google Scholar
- 18.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-188Google Scholar
- 19.Loizou C, Pattichis C (2008) Despeckle filtering algorithms and software for ultrasound imaging. Morgan and Claypool, San RafaelGoogle Scholar
- 21.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 92–103Google Scholar
- 25.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 242–251CrossRefGoogle Scholar
- 27.Shalev-Shwartz S, Zhang T (2013) Stochastic dual coordinate ascent methods for regularized loss. J Mach Learn Res 14(1):567–599Google Scholar
- 28.Shapiro R, Haralick R (1992) Computer and robot vision. Addison-Wesley, BostonGoogle Scholar
- 31.Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org/
- 32.Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224Google Scholar